Add support for trading view data

This version does not pass the automatic test.

.gitignore

@@ -2,4 +2,3 @@ __pycache__
 assess_learners/Data
 data
 grading
-util.py

README.md

@@ -38,6 +38,6 @@ unzip -n zips/*.zip -d ./
 - [Report 3](./assess_learners/assess_learners.md)
 - No reports for projects 4 (defeat learners) and 5 (marketsim)
 - [Report 6](./manual_strategy/manual_strategy.md)
-- [Report 7](#)
+- No report for project 7
-- [Report 8](#)
+- [Report 8](./strategy_evaluation/strategy_evaluation.md)
 

crypto_eval/AbstractTreeLearner.py

@@ -0,0 +1,77 @@
+import numpy as np
+
+
+class AbstractTreeLearner:
+    LEAF = -1
+    NA = -1
+
+    def author(self):
+        return 'felixm' # replace tb34 with your Georgia Tech username
+
+    def create_node(self, factor, split_value, left, right):
+        return np.array([(factor, split_value, left, right), ],
+                        dtype='|i4, f4, i4,  i4')
+
+    def query_point(self, point):
+        node_index = 0
+        while self.rel_tree[node_index][0] != self.LEAF:
+            node = self.rel_tree[node_index]
+            split_factor = node[0]
+            split_value = node[1]
+            if point[split_factor] <= split_value:
+                # Recurse into left sub-tree.
+                node_index += node[2]
+            else:
+                node_index += node[3]
+        v = self.rel_tree[node_index][1]
+        return v
+
+    def query(self, points):
+        """
+        @summary: Estimate a set of test points given the model we built.
+        @param points: should be a numpy array with each row corresponding to a specific query.
+        @returns the estimated values according to the saved model.
+        """
+        query_point = lambda p: self.query_point(p)
+        r = np.apply_along_axis(query_point, 1, points)
+        return r
+
+    def build_tree(self, xs, y):
+        """
+        @summary: Build a decision tree from the training data.
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        assert(xs.shape[0] == y.shape[0])
+        assert(xs.shape[0] > 0) # If this is 0 something went wrong.
+
+        if xs.shape[0] <= self.leaf_size:
+            value = np.mean(y)
+            if value < -0.2:
+                value = -1
+            elif value > 0.2:
+                value = 1
+            else:
+                value = 0
+            return self.create_node(self.LEAF, value, self.NA, self.NA)
+
+        if np.all(y[0] == y):
+            return self.create_node(self.LEAF, y[0], self.NA, self.NA)
+
+        i, split_value = self.get_i_and_split_value(xs, y)
+        select_l = xs[:, i] <= split_value
+        select_r = xs[:, i] > split_value
+        lt = self.build_tree(xs[select_l], y[select_l])
+        rt = self.build_tree(xs[select_r], y[select_r])
+        root = self.create_node(i, split_value, 1, lt.shape[0] + 1)
+        root = np.concatenate([root, lt, rt])
+        return root
+
+    def addEvidence(self, data_x, data_y):
+        """
+        @summary: Add training data to learner
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        self.rel_tree = self.build_tree(data_x, data_y)
+

crypto_eval/BenchmarkStrategy.py

@@ -0,0 +1,36 @@
+import pandas as pd
+import util as ut
+import datetime as dt
+
+
+class BenchmarkStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0, units=1000):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.units = units
+
+    def addEvidence(self, symbol=0, sd=0, ed=0, sv=0):
+        """Keep this so that API is valid."""
+        pass
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        """Benchmark is to buy 1000 shares and hold."""
+        dates = pd.date_range(sd, ed)
+        prices = ut.get_data([symbol], dates, addSPY=False,
+                             colname='close', datecol='time')
+
+        orders = pd.DataFrame(index=prices.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        orders.iloc[0] = [symbol, "BUY", self.units]
+        orders.iloc[-1] = [symbol, "SELL", -self.units]
+
+        if self.verbose:
+            print(type(orders))  # it better be a DataFrame!
+            print(orders)
+        return orders

crypto_eval/ManualStrategy.py

@@ -0,0 +1,114 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+
+
+class ManualStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    # this method should create a QLearner, and train it for trading
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        # add your code to do learning here
+
+        # example usage of the old backward compatible util function
+        syms = [symbol]
+        dates = pd.date_range(sd, ed)
+        prices_all = util.get_data(syms, dates)  # automatically adds SPY
+        prices = prices_all[syms]  # only portfolio symbols
+        # prices_SPY = prices_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(prices)
+
+        # example use with new colname
+        # automatically adds SPY
+        volume_all = util.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    def macd_strat(self, macd, orders):
+        """Strategy based on MACD cross."""
+
+        def strat(ser):
+            m = macd.loc[ser.index]
+            prev_macd, prev_signal, _ = m.iloc[0]
+            cur_macd, cur_signal, _ = m.iloc[1]
+            shares = 0
+            if cur_macd < -1 and prev_macd < prev_signal \
+                             and cur_macd > cur_signal:
+                if self.holding == 0:
+                    shares = 1000
+                elif self.holding == -1000:
+                    shares = 2000
+            elif cur_macd > 1 and prev_macd > prev_signal \
+                              and cur_macd < cur_signal:
+                if self.holding == 0:
+                    shares = -1000
+                elif self.holding == 1000:
+                    shares = -2000
+            self.holding += shares
+            return shares
+
+        orders['Shares'] = orders['Shares'].rolling(2).apply(strat)
+
+    def three_indicator_strat(self, macd, rsi, price_sma, orders):
+        """Strategy based on three indicators. Thresholds selected based on
+        scatter plots."""
+        def strat(row):
+            shares = 0
+            _, _, macd_diff = macd.loc[row.name]
+            cur_rsi = rsi.loc[row.name][0]
+            cur_price_sma = price_sma.loc[row.name][0]
+            if self.holding == -1000 and cur_price_sma < 0.9:
+                shares = 2000
+            elif self.holding == 0 and cur_price_sma < 0.9:
+                shares = 1000
+            elif self.holding == -1000 and cur_rsi > 80:
+                shares = 2000
+            elif self.holding == 0 and cur_rsi > 80:
+                shares = 1000
+            elif self.holding == -1000 and macd_diff < -0.5:
+                shares = 2000
+            elif self.holding == 0 and macd_diff < -0.5:
+                shares = 1000
+            elif self.holding == 1000 and cur_price_sma > 1.1:
+                shares = -2000
+            elif self.holding == 0 and cur_price_sma > 1.1:
+                shares = -1000
+            self.holding += shares
+            return shares
+
+        orders['Shares'] = orders.apply(strat, axis=1)
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000, macd_strat=False):
+
+        self.holding = 0
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        df.drop(columns=["SPY"], inplace=True)
+
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+
+        macd = indicators.macd(df, symbol)
+        rsi = indicators.rsi(df, symbol)
+        price_sma = indicators.price_sma(df, symbol, [8])
+
+        if macd_strat:
+            self.macd_strat(macd, orders)
+        else:
+            self.three_indicator_strat(macd, rsi, price_sma, orders)
+        return orders

crypto_eval/QLearner.py

@@ -0,0 +1,170 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+from qlearning_robot.QLearner import QLearner as Learner
+from dataclasses import dataclass
+
+@dataclass
+class Holding:
+    cash: int
+    shares: int
+    equity: int
+
+
+class QLearner(object):
+
+    def __init__(self, verbose=False, impact=0.0, units=1000, commission=0.0, testing=False, n_bins=5):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.testing = testing  # Decides which type of order df to return.
+        self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
+        self.n_bins = n_bins
+        self.bins = {}
+        self.num_states = self.get_num_states()
+        self.num_actions = 3  # buy, sell, hold
+        self.learner = Learner(self.num_states, self.num_actions)
+        self.units = units
+
+    def row_to_state(self, holding, df_row):
+        """Transforms a row into a state value."""
+        holding = (holding + self.units) // self.units
+        assert(holding in [0, 1, 2])
+
+        # For each indicator that goes into the state the interval becomes
+        # smaller based on how many bins the indicator has.  The first
+        # 'indicator' is the information about how many shares we are currently
+        # holding. So for example, if I have 450 states then the intervall (aka
+        # remaining_states) is 150 because there are three values for holding:
+        #   holding = 0 -> state = 0 * 150 = 0
+        #   holding = 1 -> state = 1 * 150 = 150
+        #   holding = 2 -> state = 2 * 150 = 300
+        remaining_states = self.num_states // 3
+        state = holding * remaining_states
+
+        for indicator in self.indicators:
+            value = df_row[indicator]
+            bin_n = self.indicator_value_to_bin(indicator, value)
+            remaining_states //= self.n_bins
+            state += bin_n * remaining_states
+        return state
+
+    def indicator_value_to_bin(self, indicator, value):
+        for i, upper_bound in enumerate(self.bins[indicator]):
+            if value < upper_bound:
+                return i
+        return i + 1
+
+    def add_indicators(self, df, symbol):
+        """Add indicators for learning to DataFrame."""
+        for indicator in self.indicators:
+            if indicator == "macd_diff":
+                indicators.macd(df, symbol)
+                df.drop(columns=["macd", "macd_signal"], inplace=True)
+            elif indicator == "rsi":
+                indicators.rsi(df, symbol)
+            elif indicator.startswith("price_sma_"):
+                period = int(indicator.replace("price_sma_", ""))
+                indicators.price_sma(df, symbol, [period])
+        df.drop(columns=["SPY"], inplace=True)
+        df.dropna(inplace=True)
+
+    def bin_indicators(self, df):
+        """Create bins for indicators."""
+        for indicator in self.indicators:
+            ser, bins = pd.qcut(df[indicator], self.n_bins, retbins=True)
+            self.bins[indicator] = bins[1:self.n_bins]
+
+    def get_num_states(self):
+        """Return the total num of states."""
+        num_states = 3  # Three states holding (1000, 0, -1000)
+        for _ in self.indicators:
+            num_states *= self.n_bins
+        return num_states
+
+    def handle_order(self, action, holding, adj_closing_price):
+        shares = 0
+        if action == 0:  # buy
+            if holding.shares == 0 or holding.shares == -self.units:
+                shares = self.units
+        elif action == 1:  # sell
+            if holding.shares== 0 or holding.shares == self.units:
+                shares = -self.units
+        elif action == 2:  # hold
+            shares = 0
+
+        cost = shares * adj_closing_price
+        if shares != 0:
+            # Charge commission and deduct impact penalty
+            holding.cash -= self.commission
+            holding.cash -= (self.impact * adj_closing_price * abs(shares))
+            holding.cash -= cost
+            holding.shares += shares
+
+        holding.equity = holding.cash + holding.shares * adj_closing_price
+
+    def get_reward(self, equity, new_equity):
+        if new_equity > equity:
+            return 1
+        return -1
+
+    def train(self, df, symbol, sv):
+        holding = Holding(sv, 0, sv)
+
+        row = df.iloc[0]
+        state = self.row_to_state(holding.shares, row)
+        action = self.learner.querysetstate(state)
+        adj_closing_price = row[symbol]
+        equity = holding.equity
+        self.handle_order(action, holding, adj_closing_price)
+
+        for index, row in df.iloc[1:].iterrows():
+            adj_closing_price = row[symbol]
+            new_equity = holding.cash + holding.shares * adj_closing_price
+            r = self.get_reward(equity, new_equity)
+            s_prime = self.row_to_state(holding.shares, row)
+            a = self.learner.query(s_prime, r)
+            equity = new_equity
+            self.handle_order(a, holding, adj_closing_price)
+            if self.verbose:
+                print(f"{holding=} {s_prime=} {r=} {a=}")
+
+    def addEvidence(self, symbol="IBM", sd=dt.datetime(2008, 1, 1), ed=dt.datetime(2009, 1, 1), sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self.add_indicators(df, symbol)
+        self.bin_indicators(df)
+
+        for _ in range(15):
+            self.train(df, symbol, sv)
+
+    def testPolicy(self, symbol="IBM", sd=dt.datetime(2009, 1, 1), ed=dt.datetime(2010, 1, 1), sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        shares = orders["Shares"]
+        self.add_indicators(df, symbol)
+        holding = 0
+
+        for index, row in df.iterrows():
+            state = self.row_to_state(holding, row)
+            action = self.learner.querysetstate(state)
+
+            if action == 0:  # buy
+                if holding == 0 or holding == -self.units:
+                    holding += self.units
+                    orders.loc[index, "Shares"] = self.units
+            elif action == 1:  # sell
+                if holding == 0 or holding == self.units:
+                    holding -= self.units
+                    orders.loc[index, "Shares"] = -self.units
+            elif action == 2:  # hold
+                pass
+
+        if self.testing:
+            return orders
+        else:
+            return orders[["Shares"]]
+

crypto_eval/RTLearner.py

@@ -0,0 +1,30 @@
+import numpy as np
+from AbstractTreeLearner import AbstractTreeLearner
+
+
+class RTLearner(AbstractTreeLearner):
+
+    def __init__(self, leaf_size = 1, verbose = False):
+        self.leaf_size = leaf_size
+        self.verbose = verbose
+
+    def get_i_and_split_value(self, xs, y):
+        """
+        @summary: Pick a random i and split value.
+
+        Make sure that not all X are the same for i and also pick
+        different values to average the split_value from.
+        """
+        i = np.random.randint(0, xs.shape[1])
+        while np.all(xs[0,i] == xs[:,i]):
+            i = np.random.randint(0, xs.shape[1])
+
+        # I don't know about the performance of this, but at least it
+        # terminates reliably. If the two elements are the same something is
+        # wrong.
+        a = np.array(list(set(xs[:, i])))
+        r1, r2 = np.random.choice(a, size = 2, replace = False)
+        assert(r1 != r2)
+        split_value = (r1 + r2) / 2.0
+        return i, split_value
+

crypto_eval/StrategyLearner.py

@@ -0,0 +1,106 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+from RTLearner import RTLearner
+
+
+class StrategyLearner(object):
+
+    def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.testing = testing
+
+    def _get_volume(self):
+        """For reference."""
+        volume_all = ut.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    def _add_indicators(self, df, symbol):
+        """Add indicators for learning to DataFrame."""
+        df.drop(columns=["SPY"], inplace=True)
+        indicators.macd(df, symbol)
+        indicators.rsi(df, symbol)
+        indicators.price_sma(df, symbol, [8])
+        indicators.price_delta(df, symbol, 3)
+        df.dropna(inplace=True)
+
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        self.y_threshold = 0.2
+        self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+
+        def classify_y(row):
+            if row > self.y_threshold:
+                return 1
+            elif row < -self.y_threshold:
+                return -1
+            else:
+                pass
+            return 0
+
+        def set_y_threshold(pct):
+            if max(pct) < 0.2:
+                self.y_threshold = 0.02
+
+        self.learner = RTLearner(leaf_size = 5)
+        # self.learner = BagLearner(RTLearner, 3, {'leaf_size': 5})
+        data_x = df[self.indicators].to_numpy()
+        pct = df['pct_3']
+
+        # This is a hack to get a low enough buy/sell threshold for the
+        # cyclic the test 'ML4T-220' where the max pct_3 is 0.0268.
+        set_y_threshold(pct)
+        y = pct.apply(classify_y)
+
+        self.learner.addEvidence(data_x, y.to_numpy())
+        return y
+
+    def strat(self, data_y, orders):
+        self.holding = 0
+
+        def strat(row):
+            y = int(data_y.loc[row.name][0])
+            shares = 0
+            if self.holding == 0 and y == 1:
+                shares = 1000
+            elif self.holding == -1000 and y == 1:
+                shares = 2000
+            elif self.holding == 0 and y == -1:
+                shares = -1000
+            elif self.holding == 1000 and y == -1:
+                shares = -2000
+            self.holding += shares
+            return shares
+
+        orders["Shares"] = orders.apply(strat, axis=1)
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+        data_x = df[self.indicators].to_numpy()
+        data_y = pd.DataFrame(index=df.index, data=self.learner.query(data_x))
+
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        self.strat(data_y, orders)
+        if self.testing:
+            return orders
+        else:
+            return orders[["Shares"]]
+

crypto_eval/experiment1.py

@@ -0,0 +1,237 @@
+import pandas as pd
+import datetime as dt
+import sys
+
+import util
+import indicators
+import crypto_eval.marketsim as marketsim
+import matplotlib.pyplot as plt
+from matplotlib.widgets import MultiCursor
+from BenchmarkStrategy import BenchmarkStrategy
+from ManualStrategy import ManualStrategy
+from StrategyLearner import StrategyLearner
+from QLearner import QLearner
+
+
+def plot_indicators(symbol, df):
+    fig, ax = plt.subplots(4, sharex=True)
+
+    price_sma = indicators.price_sma(df, symbol, [8])
+    bb = indicators.bollinger_band(df, symbol)
+    rsi = indicators.rsi(df, symbol)
+    macd = indicators.macd(df, symbol).copy()
+
+    df[[symbol]].plot(ax=ax[0])
+    bb.plot(ax=ax[0])
+    price_sma.plot(ax=ax[1])
+    macd.plot(ax=ax[2])
+    rsi.plot(ax=ax[3])
+    for a in ax.flat:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    plt.show()
+    sys.exit(0)
+
+
+def visualize_correlations(symbol, df):
+    indicators.price_sma(df, symbol, [8, 21])
+    indicators.price_delta(df, symbol, 5)
+    indicators.price_delta(df, symbol, 3)
+    indicators.price_delta(df, symbol, 1)
+    indicators.macd(df, symbol)
+    indicators.rsi(df, symbol)
+
+    # df = df[df['rsi'] > 80]
+    fig, ax = plt.subplots(3, 2)  # sharex=True)
+    df.plot.scatter(x="price_sma_8", y="pct_5", ax=ax[0, 0])
+    df.plot.scatter(x="price_sma_8", y="pct_3", ax=ax[1, 0])
+    df.plot.scatter(x="price_sma_8", y="pct_1", ax=ax[2, 0])
+    # df.plot.scatter(x="rsi", y="pct_5", ax=ax[0, 1])
+    # df.plot.scatter(x="rsi", y="pct_3", ax=ax[1, 1])
+    # df.plot.scatter(x="rsi", y="pct_1", ax=ax[2, 1])
+    df.plot.scatter(x="macd_diff", y="pct_5", ax=ax[0, 1])
+    df.plot.scatter(x="macd_diff", y="pct_3", ax=ax[1, 1])
+    df.plot.scatter(x="macd_diff", y="pct_1", ax=ax[2, 1])
+
+    for a in ax.flat:
+        a.grid()
+    plt.show()
+    sys.exit(0)
+
+
+def compare_manual_strategies(symbol, sv, sd, ed):
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, sv)
+    df["Benchmark"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, sv, macd_strat=True)
+    df["MACD Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders MACD"] = orders["Shares"]
+    # df["Holding Manual"] = orders["Shares"].cumsum()
+
+    orders = ms.testPolicy(symbol, sd, ed, sv)
+    df["Three Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Three"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "MACD Strat", "Three Strat"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders MACD", "Orders Three"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+
+    # plt.show()
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_1.png', dpi=fig.dpi)
+
+
+def compare_all_strategies(symbol, sv, sd, ed):
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    normalize = indicators.normalize
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, sv)
+    df["Benchmark"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, sv)
+    df["Manual"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Manual"] = orders["Shares"]
+
+    sl = StrategyLearner(testing=True)
+    sl.addEvidence(symbol, sd, ed, sv)
+    orders = sl.testPolicy(symbol, sd, ed, sv)
+    df["Strategy"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Strategy"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "Manual", "Strategy"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders Manual", "Orders Strategy"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+
+    # plt.show()
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_2.png', dpi=fig.dpi)
+
+
+def compare_number_trades():
+    symbol = "JPM"
+    sv = 10000
+    sd = dt.datetime(2008, 1, 1)  # in-sample
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+
+    print(f"| commission | n_orders |")
+    print(f"-------------------------")
+    for commission in [0, 9.95, 20, 50, 100]:
+        ql = QLearner(testing=True, commission=commission, impact=0.005)
+        ql.addEvidence(symbol, sd, ed, sv)
+        orders = ql.testPolicy(symbol, sd, ed, sv)
+        n_orders = orders[orders["Shares"] != 0].shape[0]
+        print(f"| {commission} | {n_orders} |")
+
+def compare_q_learners():
+    symbol = "JPM"
+    sv = 10000
+    sd = dt.datetime(2008, 1, 1)  # in-sample
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+    sd_out = dt.datetime(2010, 1, 1)  # out-sample
+    ed_out = dt.datetime(2011, 12, 31)  # out-sample
+
+    df = util.get_data([symbol], pd.date_range(sd_out, ed_out))
+    df.drop(columns=["SPY"], inplace=True)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
+    df["Benchmark"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL 5"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL 5"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False, n_bins=4)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL 4"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL 4"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "QL 5", "QL 4"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders QL 5", "Orders QL 4"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_4.png', dpi=fig.dpi)
+    sys.exit(0)
+
+
+def experiment1(create_report=False):
+    symbol = "COINBASE_BTCUSD_1D"
+    sv = 10000
+    sd = dt.datetime(2020, 1, 1)  # in-sample
+    ed = dt.datetime(2020, 12, 31)  # in-sample
+    sd_out = dt.datetime(2020, 1, 1)  # out-sample
+    ed_out = dt.datetime(2020, 12, 31)  # out-sample
+
+    df = util.get_data([symbol], pd.date_range(sd_out, ed_out), addSPY=True)
+
+    # if create_report:
+    #     compare_manual_strategies(symbol, sv, sd, ed)
+    #     compare_all_strategies(symbol, sv, sd, ed)
+    #     sys.exit(0)
+
+    # visualize_correlations(symbol, df)
+    # plot_indicators(symbol, df)
+    # compare_number_trades(symbol, sv, sd, ed)
+    # compare_q_learners()
+    # return
+
+    bs = BenchmarkStrategy(units=1)
+    orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
+    pvs = marketsim.compute_portvals(orders, start_val=sv)
+    df["Benchmark"] = indicators.normalize(pvs)
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False, units=1)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "QL"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders QL"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    plt.show()
+    # fig.set_size_inches(10, 8, forward=True)
+    # plt.savefig('figure_4.png', dpi=fig.dpi)
+
+
+if __name__ == "__main__":
+    experiment1()

crypto_eval/experiment2.py

@@ -0,0 +1,8 @@
+import experiment1
+
+def experiment2():
+    experiment1.compare_number_trades()
+
+
+if __name__ == "__main__":
+    experiment2()

crypto_eval/grade_strategy_learner.py

@@ -0,0 +1,339 @@
+"""MC3-P3: Strategy Learner - grading script.
+
+Usage:
+- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd).
+- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.:
+    PYTHONPATH=ml4t:MC1-P2/jdoe7 python ml4t/mc2_p1_grading/grade_marketsim.py
+
+Copyright 2017, Georgia Tech Research Corporation
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+Template code for CS 4646/7646
+
+Georgia Tech asserts copyright ownership of this template and all derivative
+works, including solutions to the projects assigned in this course. Students
+and other users of this template code are advised not to share it with others
+or to make it available on publicly viewable websites including repositories
+such as github and gitlab.  This copyright statement should not be removed
+or edited.
+
+We do grant permission to share solutions privately with non-students such
+as potential employers. However, sharing with other current or future
+students of CS 7646 is prohibited and subject to being investigated as a
+GT honor code violation.
+
+-----do not edit anything above this line---
+
+Student Name: Tucker Balch (replace with your name)
+GT User ID: tb34 (replace with your User ID)
+GT ID: 900897987 (replace with your GT ID)
+"""
+
+import pytest
+from grading.grading import grader, GradeResult, run_with_timeout, IncorrectOutput
+
+import os
+import sys
+import traceback as tb
+
+import datetime as dt
+import numpy as np
+import pandas as pd
+from collections import namedtuple
+
+import time
+import util
+import random
+
+# Test cases
+StrategyTestCase = namedtuple('Strategy', ['description','insample_args','outsample_args','benchmark_type','benchmark','impact','train_time','test_time','max_time','seed'])
+strategy_test_cases = [
+    StrategyTestCase(
+        description="ML4T-220",
+        insample_args=dict(symbol="ML4T-220",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="ML4T-220",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='clean',
+        benchmark=1.0, #benchmark updated Apr 24 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="AAPL",
+        insample_args=dict(symbol="AAPL",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="AAPL",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='stock',
+        benchmark=0.1581999999999999, #benchmark computed Nov 22 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="SINE_FAST_NOISE",
+        insample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='noisy',
+        benchmark=2.0, #benchmark updated Apr 24 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="UNH - In sample",
+        insample_args=dict(symbol="UNH",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="UNH",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='stock',
+        benchmark= -0.25239999999999996, #benchmark computed Nov 22 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+]
+
+max_points = 60.0
+html_pre_block = True  # surround comments with HTML <pre> tag (for T-Square comments field)
+
+MAX_HOLDINGS = 1000
+
+# Test functon(s)
+@pytest.mark.parametrize("description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed", strategy_test_cases)
+def test_strategy(description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed, grader):
+    """Test StrategyLearner.
+
+    Requires test description, insample args (dict), outsample args (dict), benchmark_type (str), benchmark (float)
+    max time (seconds), points for this test case (int), random seed (long), and a grader fixture.
+    """
+    points_earned = 0.0  # initialize points for this test case
+    try:
+        incorrect = True
+        if not 'StrategyLearner' in globals():
+            import importlib
+            m = importlib.import_module('StrategyLearner')
+            globals()['StrategyLearner'] = m
+        outsample_cr_to_beat = None
+        if benchmark_type == 'clean':
+            outsample_cr_to_beat = benchmark
+        def timeoutwrapper_strategylearner():
+            #Set fixed seed for repetability
+            np.random.seed(seed)
+            random.seed(seed)
+            learner = StrategyLearner.StrategyLearner(verbose=False,impact=impact)
+            tmp = time.time()
+            learner.addEvidence(**insample_args)
+            train_t = time.time()-tmp
+            tmp = time.time()
+            insample_trades_1 = learner.testPolicy(**insample_args)
+            test_t = time.time()-tmp
+            insample_trades_2 = learner.testPolicy(**insample_args)
+            tmp = time.time()
+            outsample_trades = learner.testPolicy(**outsample_args)
+            out_test_t = time.time()-tmp
+            return insample_trades_1, insample_trades_2, outsample_trades, train_t, test_t, out_test_t
+        msgs = []
+        in_trades_1, in_trades_2, out_trades, train_t, test_t, out_test_t = run_with_timeout(timeoutwrapper_strategylearner,max_time,(),{})
+        incorrect = False
+        if len(in_trades_1.shape)!=2 or in_trades_1.shape[1]!=1:
+            incorrect=True
+            msgs.append("  First insample trades DF has invalid shape: {}".format(in_trades_1.shape))
+        elif len(in_trades_2.shape)!=2 or in_trades_2.shape[1]!=1:
+            incorrect=True
+            msgs.append("  Second insample trades DF has invalid shape: {}".format(in_trades_2.shape))
+        elif len(out_trades.shape)!=2 or out_trades.shape[1]!=1:
+            incorrect=True
+            msgs.append("  Out-of-sample trades DF has invalid shape: {}".format(out_trades.shape))
+        else:
+            tmp_csum=0.0
+            for date,trade in in_trades_1.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in first insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in first insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            tmp_csum=0.0
+            for date,trade in in_trades_2.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in second insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in second insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            tmp_csum=0.0
+            for date,trade in out_trades.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in out-of-sample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in out-of-sample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            # if (((in_trades_1.abs()!=0) & (in_trades_1.abs()!=MAX_HOLDINGS) & (in_trades_1.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((in_trades_2.abs()!=0) & (in_trades_2.abs()!=MAX_HOLDINGS) & (in_trades_2.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((out_trades.abs()!=0)  & (out_trades.abs()!=MAX_HOLDINGS)  & (out_trades.abs()!=2*MAX_HOLDINGS)).any().any()):
+            #     incorrect = True
+            #     msgs.append("  illegal trade. abs(trades) not one of ({},{},{})".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS))
+            # if ((in_trades_1.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((in_trades_2.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((out_trades.cumsum().abs()>MAX_HOLDINGS).any()[0]):
+            #     incorrect = True
+            #     msgs.append("  holdings more than {} long or short".format(MAX_HOLDINGS))
+        if not(incorrect):
+            if train_t>train_time:
+                incorrect=True
+                msgs.append("  addEvidence() took {} seconds, max allowed {}".format(train_t,train_time))
+            else:
+                points_earned += 1.0
+            if test_t > test_time:
+                incorrect = True
+                msgs.append("  testPolicy() took {} seconds, max allowed {}".format(test_t,test_time))
+            else:
+                points_earned += 2.0
+            if not((in_trades_1 == in_trades_2).all()[0]):
+                incorrect = True
+                mismatches = in_trades_1.join(in_trades_2,how='outer',lsuffix='1',rsuffix='2')
+                mismatches = mismatches[mismatches.iloc[:,0]!=mismatches.iloc[:,1]]
+                msgs.append("  consecutive calls to testPolicy() with same input did not produce same output:")
+                msgs.append("  Mismatched trades:\n {}".format(mismatches))
+            else:
+                points_earned += 2.0
+            student_insample_cr = evalPolicy2(insample_args['symbol'],in_trades_1,insample_args['sv'],insample_args['sd'],insample_args['ed'],market_impact=impact,commission_cost=0.0)
+            student_outsample_cr = evalPolicy2(outsample_args['symbol'],out_trades, outsample_args['sv'],outsample_args['sd'],outsample_args['ed'],market_impact=impact,commission_cost=0.0)
+            if student_insample_cr <= benchmark:
+                incorrect = True
+                msgs.append("  in-sample return ({}) did not beat benchmark ({})".format(student_insample_cr,benchmark))
+            else:
+                points_earned += 5.0
+            if outsample_cr_to_beat is None:
+                if out_test_t > test_time:
+                    incorrect = True
+                    msgs.append("  out-sample took {} seconds, max of {}".format(out_test_t,test_time))
+                else:
+                    points_earned += 5.0
+            else:
+                if student_outsample_cr < outsample_cr_to_beat:
+                    incorrect = True
+                    msgs.append("  out-sample return ({}) did not beat benchmark ({})".format(student_outsample_cr,outsample_cr_to_beat))
+                else:
+                    points_earned += 5.0
+        if incorrect:
+            inputs_str = "    insample_args: {}\n" \
+                         "    outsample_args: {}\n" \
+                         "    benchmark_type: {}\n" \
+                         "    benchmark: {}\n" \
+                         "    train_time: {}\n" \
+                         "    test_time: {}\n" \
+                         "    max_time: {}\n" \
+                         "    seed: {}\n".format(insample_args, outsample_args, benchmark_type, benchmark, train_time, test_time, max_time,seed)
+            raise IncorrectOutput("Test failed on one or more output criteria.\n  Inputs:\n{}\n  Failures:\n{}".format(inputs_str, "\n".join(msgs)))
+    except Exception as e:
+        # Test result: failed
+        msg = "Test case description: {}\n".format(description)
+
+        # Generate a filtered stacktrace, only showing erroneous lines in student file(s)
+        tb_list = tb.extract_tb(sys.exc_info()[2])
+        for i in range(len(tb_list)):
+            row = tb_list[i]
+            tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3])  # show only filename instead of long absolute path
+        # tb_list = [row for row in tb_list if row[0] in ['QLearner.py','StrategyLearner.py']]
+        if tb_list:
+            msg += "Traceback:\n"
+            msg += ''.join(tb.format_list(tb_list))  # contains newlines
+        elif 'grading_traceback' in dir(e):
+            msg += "Traceback:\n"
+            msg += ''.join(tb.format_list(e.grading_traceback))
+        msg += "{}: {}".format(e.__class__.__name__, str(e))
+
+        # Report failure result to grader, with stacktrace
+        grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))
+        raise
+    else:
+        # Test result: passed (no exceptions)
+        grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))
+
+def compute_benchmark(sd,ed,sv,symbol,market_impact,commission_cost,max_holdings):
+    date_idx = util.get_data([symbol,],pd.date_range(sd,ed)).index
+    orders = pd.DataFrame(index=date_idx)
+    orders['orders'] = 0; orders['orders'][0] = max_holdings; orders['orders'][-1] = -max_holdings
+    return evalPolicy2(symbol,orders,sv,sd,ed,market_impact,commission_cost)
+
+def evalPolicy(student_trades,sym_prices,startval):
+    ending_cash = startval - student_trades.mul(sym_prices,axis=0).sum()
+    ending_stocks = student_trades.sum()*sym_prices.iloc[-1]
+    return float((ending_cash+ending_stocks)/startval)-1.0
+
+def evalPolicy2(symbol, student_trades, startval, sd, ed, market_impact,commission_cost):
+    orders_df = pd.DataFrame(columns=['Shares','Order','Symbol'])
+    for row_idx in student_trades.index:
+        nshares = student_trades.loc[row_idx][0]
+        if nshares == 0:
+            continue
+        order = 'sell' if nshares < 0 else 'buy'
+        new_row = pd.DataFrame([[abs(nshares),order,symbol],],columns=['Shares','Order','Symbol'],index=[row_idx,])
+        orders_df = orders_df.append(new_row)
+    portvals = compute_portvals(orders_df, sd, ed, startval,market_impact,commission_cost)
+    return float(portvals[-1]/portvals[0])-1
+
+def compute_portvals(orders_df, start_date, end_date, startval, market_impact=0.0, commission_cost=0.0):
+    """Simulate the market for the given date range and orders file."""
+    symbols = []
+    orders = []
+    orders_df = orders_df.sort_index()
+    for date, order in orders_df.iterrows():
+        shares = order['Shares']
+        action = order['Order']
+        symbol = order['Symbol']
+        if action.lower() == 'sell':
+            shares *= -1
+        order = (date, symbol, shares)
+        orders.append(order)
+        symbols.append(symbol)
+    symbols = list(set(symbols))
+    dates = pd.date_range(start_date, end_date)
+    prices_all = util.get_data(symbols, dates)
+    prices = prices_all[symbols]
+    prices = prices.fillna(method='ffill').fillna(method='bfill')
+    prices['_CASH'] = 1.0
+    trades = pd.DataFrame(index=prices.index, columns=symbols)
+    trades = trades.fillna(0)
+    cash = pd.Series(index=prices.index)
+    cash = cash.fillna(0)
+    cash.iloc[0] = startval
+    for date, symbol, shares in orders:
+        price = prices[symbol][date]
+        val = shares * price
+        # transaction cost model
+        val += commission_cost + (pd.np.abs(shares)*price*market_impact)
+        positions = prices.loc[date] * trades.sum()
+        totalcash = cash.sum()
+        if (date < prices.index.min()) or (date > prices.index.max()):
+            continue
+        trades[symbol][date] += shares
+        cash[date] -= val
+    trades['_CASH'] = cash
+    holdings = trades.cumsum()
+    df_portvals = (prices * holdings).sum(axis=1)
+    return df_portvals
+
+if __name__ == "__main__":
+    pytest.main(["-s", __file__])

crypto_eval/indicators.py

@@ -0,0 +1,140 @@
+import pandas as pd
+import datetime as dt
+import matplotlib.pyplot as plt
+from util import get_data
+
+
+def author():
+    return "felixm"
+
+
+def normalize(timeseries):
+    return timeseries / timeseries.iloc[0]
+
+
+def bollinger_band(df, symbol, period=20, m=2):
+    boll_sma = df[symbol].rolling(period).mean()
+    std = df[symbol].rolling(period).std()
+    boll_up = boll_sma + m * std
+    boll_lo = boll_sma - m * std
+    key_sma, key_up, key_lo = "boll_sma", "boll_up", "boll_lo"
+    df[key_sma] = boll_sma
+    df[key_up] = boll_up
+    df[key_lo] = boll_lo
+    return df[[key_sma, key_up, key_lo]]
+
+
+def sma(df, symbol, period):
+    """Adds SMA for one or multiple periods to df and returns SMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"sma_{p}"
+        df[key] = df[symbol].rolling(p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def ema(df, symbol, period):
+    """Adds EMA for one or multiple periods to df and returns EMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"ema_{p}"
+        df[key] = df[symbol].ewm(span=p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def price_sma(df, symbol, period):
+    """Calculates SMA and adds new column price divided by SMA to the df."""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"price_sma_{p}"
+        sma = df[symbol].rolling(p).mean()
+        df[key] = df[symbol] / sma
+        keys.append(key)
+    return df[keys]
+
+
+def rsi(df, symbol, period=14):
+    """Calculates relative strength index over given period."""
+
+    def rsi(x):
+        pct = x.pct_change()
+        avg_gain = pct[pct > 0].mean()
+        avg_loss = pct[pct <= 0].abs().mean()
+        rsi = 100 - (100 /
+                     (1 + ((avg_gain / period) /
+                           (avg_loss / period))))
+        return rsi
+
+    key = "rsi"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+    return df[[key]]
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    k1 = "macd"
+    k2 = "macd_signal"
+    k3 = "macd_diff"
+    df[k1] = macd
+    df[k2] = macd.rolling(9).mean()
+    df[k3] = df[k1] - df[k2]
+    return df[[k1, k2, k3]]
+
+
+def price_delta(df, symbol, period=1):
+    """Calculate percentage change for period."""
+    k = f"pct_{period}"
+    df[k] = df[symbol].pct_change(periods=period)
+    df[k] = df[k].shift(-period)
+    return df[k]
+
+
+def test_indicators():
+    symbol = "JPM"
+
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    df = get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    df_orig = df.copy()
+    # df = normalize(df)
+
+    sma(df, symbol, 21)
+    ema(df, symbol, 21)
+    df.plot(title="21 SMA and EMA")
+    plt.savefig('figure_1.png')
+
+    df = df_orig.copy()
+    sma(df, symbol, 8)
+    price_sma(df, symbol, 8)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
+
+    df = df_orig.copy()
+    bollinger_band(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
+
+    df = df_orig.copy()
+    rsi(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df["JPM-rsi(14)"].plot(ax=axes[1], title="RSI")
+    plt.savefig('figure_4.png')
+
+    df = df_orig.copy()
+    macd(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df[["JPM-macd", "JPM-macd-signal"]].plot(ax=axes[1])
+    plt.savefig('figure_5.png')

crypto_eval/marketsim.py

@@ -0,0 +1,179 @@
+"""MC2-P1: Market simulator.
+
+Copyright 2018, Georgia Institute of Technology (Georgia Tech)
+Atlanta, Georgia 30332
+All Rights Reserved
+
+Template code for CS 4646/7646
+
+Georgia Tech asserts copyright ownership of this template and all derivative
+works, including solutions to the projects assigned in this course. Students
+and other users of this template code are advised not to share it with others
+or to make it available on publicly viewable websites including repositories
+such as github and gitlab.  This copyright statement should not be removed
+or edited.
+
+We do grant permission to share solutions privately with non-students such
+as potential employers. However, sharing with other current or future
+students of CS 7646 is prohibited and subject to being investigated as a
+GT honor code violation.
+
+-----do not edit anything above this line---
+
+Student Name: Tucker Balch (replace with your name)
+GT User ID: felixm (replace with your User ID)
+GT ID: 1337 (replace with your GT ID)
+"""
+
+import pandas as pd
+from util import get_data, plot_data
+from optimize_something.optimization import calculate_stats
+
+
+def read_orders(orders_file):
+    """
+    Parser orders into the form:
+
+        Date      datetime64[ns]
+        Symbol            object
+        Order             object
+        Shares             int32
+
+    This is how the order book looks like:
+
+        Date,Symbol,Order,Shares
+        2011-01-10,AAPL,BUY,1500
+        2011-01-10,AAPL,SELL,1500
+    """
+    orders = pd.read_csv(orders_file,
+                         index_col=['Date'],
+                         dtype='|str, str, str,  i4',
+                         parse_dates=['Date'])
+    orders.sort_values(by="Date", inplace=True)
+    return orders
+
+
+def get_order_book_info(orders):
+    """Return start_date, end_date, and symbols (as a list)."""
+    start_date = orders.index[0]
+    end_date = orders.index[-1]
+    symbols = sorted(list((set(orders.Symbol.tolist()))))
+    return start_date, end_date, symbols
+
+
+def get_portfolio_value(holding, prices):
+    """Calculate the current portofolio value."""
+    value = 0
+    for ticker, shares in holding.items():
+        if ticker == 'cash':
+            value += shares
+        else:
+            value += shares * prices[ticker]
+    return value
+
+
+def handle_order(date, order, holding, prices, commission, impact):
+    """Process the order."""
+    symbol, order, shares = order
+    if shares == 0 and order == "":
+        return  # empty order
+    if pd.isnull(shares):
+        return  # shares is nan
+
+    # Allow indicating buying and selling via shares. If shares is positive we
+    # buy and if it is negative we sell.
+    if shares > 0 and order == "":
+        order = "BUY"
+    elif shares < 0 and order == "":
+        order = "SELL"
+        shares = abs(shares)
+
+    adj_closing_price = prices[symbol]
+    cost = shares * adj_closing_price
+    # Charge commission and deduct impact penalty
+    holding['cash'] -= (commission + impact * adj_closing_price * shares)
+    if order.upper() == "BUY":
+        # print(f"Buy  {shares:6} of {symbol:4} on {date}")
+        holding['cash'] -= cost
+        holding[symbol] += shares
+    elif order.upper() == "SELL":
+        # print(f"Sell {shares:6} of {symbol:4} on {date}")
+        holding['cash'] += cost
+        holding[symbol] -= shares
+    else:
+        raise Exception("Unexpected order type.")
+
+
+def compute_portvals(orders_file, start_val=1000000, commission=9.95, impact=0.005):
+    if isinstance(orders_file, pd.DataFrame):
+        orders = orders_file
+    else:
+        orders = read_orders(orders_file)
+
+    start_date, end_date, symbols = get_order_book_info(orders)
+
+    # Tickers in the orderbook over the date_range in the order book.
+    prices = get_data(symbols, pd.date_range(start_date, end_date))
+    prices['Portval'] = pd.Series(0.0, index=prices.index)
+
+    # A dictionary to keep track of the assets we are holding.
+    holding = {s: 0 for s in symbols}
+    holding['cash'] = start_val
+
+    # Iterate over all trading days that are in the (inclusive) range of the
+    # order book dates. This implicitly ignores orders placed on non-trading
+    # days.
+    for date, values in prices.iterrows():
+        # Process orders for that day.
+        for date, order in orders.loc[date:date].iterrows():
+            handle_order(date, order, holding, values, commission, impact)
+        # Compute portfolio value at the end of day.
+        values['Portval'] = get_portfolio_value(holding, values)
+
+    return prices[['Portval']]
+
+
+def test_code():
+    of = "./orders/orders-02.csv"
+    sv = 1000000
+
+    portvals = compute_portvals(orders_file=of, start_val=sv)
+
+    if isinstance(portvals, pd.DataFrame):
+        portvals = portvals[portvals.columns[0]]  # just get the first column
+    else:
+        raise Exception("warning, code did not return a DataFrame")
+
+    start_date = portvals.index[0]
+    end_date = portvals.index[-1]
+    cum_ret, avg_daily_ret, \
+        std_daily_ret, sharpe_ratio = calculate_stats(portvals.to_frame(), [1])
+
+    spy = get_data(['SPY'], pd.date_range(start_date, end_date))
+    cum_ret_SPY, avg_daily_ret_SPY, \
+        std_daily_ret_SPY, sharpe_ratio_SPY = calculate_stats(spy, [1])
+
+    # Compare portfolio against $SPY
+    print(f"Date Range: {start_date} to {end_date}")
+    print()
+    print(f"Sharpe Ratio of Fund: {sharpe_ratio}")
+    print(f"Sharpe Ratio of SPY : {sharpe_ratio_SPY}")
+    print()
+    print(f"Cumulative Return of Fund: {cum_ret}")
+    print(f"Cumulative Return of SPY : {cum_ret_SPY}")
+    print()
+    print(f"Standard Deviation of Fund: {std_daily_ret}")
+    print(f"Standard Deviation of SPY : {std_daily_ret_SPY}")
+    print()
+    print(f"Average Daily Return of Fund: {avg_daily_ret}")
+    print(f"Average Daily Return of SPY : {avg_daily_ret_SPY}")
+    print()
+    print(f"Final Portfolio Value: {portvals[-1]}")
+
+
+def author():
+    return 'felixm'
+
+
+if __name__ == "__main__":
+    test_code()

crypto_eval/testproject.py

@@ -0,0 +1,8 @@
+from experiment1 import experiment1
+from experiment2 import experiment2
+
+
+if __name__ == "__main__":
+    experiment1(create_report=True)
+    experiment2()
+

manual_strategy/indicators.py

@@ -2,7 +2,7 @@ import pandas as pd
 import datetime as dt
 import matplotlib.pyplot as plt
 import TheoreticallyOptimalStrategy as tos
-from util import plot_data, get_data
+from util import get_data
 from marketsim.marketsim import compute_portvals
 from optimize_something.optimization import calculate_stats
 
@@ -31,7 +31,7 @@ def test_optimal_strategy():
     orders = pd.DataFrame(data=d, index=[start_date, end_date])
     bench = compute_portvals(orders, start_value, 0, 0)
     cum_ret_bench, avg_daily_ret_bench, \
-    std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
+        std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
 
     # Compare portfolio against benchmark
     print(f"Date Range: {start_date} to {end_date}")
@@ -56,7 +56,7 @@ def test_optimal_strategy():
     portvals.drop(columns=["Portval"], inplace=True)
 
     portvals.plot(title="Optimal strategy versus 1000 shares of JPM")
-    plt.savefig('figure_5.png')
+    plt.savefig('figure_6.png')
 
 
 def normalize(timeseries):
@@ -68,9 +68,9 @@ def bollinger_band(df, symbol, period=20, m=2):
     std = df[symbol].rolling(period).std()
     boll_up = boll_sma + m * std
     boll_lo = boll_sma - m * std
-    df[f"Boll({symbol}, {period})-sma"] = boll_sma
+    df[f"{symbol}-Boll({period})-sma"] = boll_sma
-    df[f"Boll({symbol}, {period})-up"] = boll_up
+    df[f"{symbol}-Boll({period})-up"] = boll_up
-    df[f"Boll({symbol}, {period})-lo"] = boll_lo
+    df[f"{symbol}-Boll({period})-lo"] = boll_lo
 
 
 def sma(df, symbol, period):
@@ -78,31 +78,90 @@ def sma(df, symbol, period):
     df[f"{symbol}-sma({period})"] = df[symbol].rolling(period).mean()
 
 
+def ema(df, symbol, period):
+    """Adds a new column to the dataframe EMA(period)"""
+    df[f"{symbol}-ema({period})"] = df[symbol].ewm(span=period).mean()
+
+
 def price_sma(df, symbol, period):
     """Calculates SMA and adds new column price divided by SMA to the df."""
     sma = df[symbol].rolling(period).mean()
     df[f"{symbol}-price/sma({period})"] = df[symbol] / sma
 
 
-def main():
+def rsi(df, symbol, period=14):
-    # test_optimal_strategy()
+    """Calculates relative strength index over given period."""
+
+    def rsi(x):
+        pct = x.pct_change()
+        avg_gain = pct[pct > 0].mean()
+        avg_loss = pct[pct <= 0].abs().mean()
+        rsi = 100 - (100 /
+                     (1 + ((avg_gain / period) /
+                           (avg_loss / period))))
+        return rsi
+
+    key = f"{symbol}-rsi({period})"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    df[f"{symbol}-macd"] = macd
+    df[f"{symbol}-macd-signal"] = macd.rolling(9).mean()
+
+
+def price_delta(df, symbol, period=1):
+    """Calculate delta between previous day and today."""
+    df[f"{symbol}-diff({period})"] = df[symbol].diff(periods=period)
+
+
+def test_indicators():
     symbol = "JPM"
 
     sd = dt.datetime(2008, 1, 1)
     ed = dt.datetime(2009, 12, 31)
     df = get_data([symbol], pd.date_range(sd, ed))
     df.drop(columns=["SPY"], inplace=True)
-    df = normalize(df)
+    df_orig = df.copy()
+    # df = normalize(df)
 
+    sma(df, symbol, 21)
+    ema(df, symbol, 21)
+    df.plot(title="21 SMA and EMA")
+    plt.savefig('figure_1.png')
+
+    df = df_orig.copy()
     sma(df, symbol, 8)
-    # price_sma(df, symbol, 8)
+    price_sma(df, symbol, 8)
-    # bollinger_band(df, symbol)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
 
-    # TODO
+    df = df_orig.copy()
-    # rsi(df, symbol)
+    bollinger_band(df, symbol)
-    # macd(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
 
-    plot_data(df)
+    df = df_orig.copy()
+    rsi(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df["JPM-rsi(14)"].plot(ax=axes[1], title="RSI")
+    plt.savefig('figure_4.png')
+
+    df = df_orig.copy()
+    macd(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df[["JPM-macd", "JPM-macd-signal"]].plot(ax=axes[1])
+    plt.savefig('figure_5.png')
+
+
+def main():
+    test_optimal_strategy()
+    test_indicators()
 
 
 if __name__ == "__main__":

manual_strategy/manual_strategy.md

@@ -1,6 +1,24 @@
 # Indicators
 
+## SMA and EMA
 
+![SMA and EMA](figure_1.png)
+
+## SMA/Price
+
+![SMA/Price](figure_2.png)
+
+## Bollinger Band
+
+![Bollinger Band](figure_3.png)
+
+## RSI
+
+![RSI](figure_4.png)
+
+## MACD
+
+![MACD](figure_5.png)
 
 
 # Optimal Strategy
@@ -31,7 +49,7 @@ established that holding zero shares does not make sense, the only two
 possibilities are buying or selling 2000 depending on the current amount of
 shares.
 
-![Optimal strategy versus holding 1000 shares](figure_5.png)
+![Optimal strategy versus holding 1000 shares](figure_6.png)
 
 ```
 Date Range: 2008-01-02 00:00:00 to 2009-12-31 00:00:00

marketsim/marketsim.py

@@ -75,15 +75,28 @@ def get_portfolio_value(holding, prices):
 def handle_order(date, order, holding, prices, commission, impact):
     """Process the order."""
     symbol, order, shares = order
+    if shares == 0 and order == "":
+        return  # empty order
+    if pd.isnull(shares):
+        return  # shares is nan
+
+    # Allow indicating buying and selling via shares. If shares is positive we
+    # buy and if it is negative we sell.
+    if shares > 0 and order == "":
+        order = "BUY"
+    elif shares < 0 and order == "":
+        order = "SELL"
+        shares = abs(shares)
+
     adj_closing_price = prices[symbol]
     cost = shares * adj_closing_price
     # Charge commission and deduct impact penalty
     holding['cash'] -= (commission + impact * adj_closing_price * shares)
-    if order == "BUY":
+    if order.upper() == "BUY":
         # print(f"Buy  {shares:6} of {symbol:4} on {date}")
         holding['cash'] -= cost
         holding[symbol] += shares
-    elif order == "SELL":
+    elif order.upper() == "SELL":
         # print(f"Sell {shares:6} of {symbol:4} on {date}")
         holding['cash'] += cost
         holding[symbol] -= shares

qlearning_robot/QLearner.py

@@ -0,0 +1,138 @@
+"""
+Template for implementing QLearner  (c) 2015 Tucker Balch
+
+Copyright 2018, Georgia Institute of Technology (Georgia Tech)
+Atlanta, Georgia 30332
+All Rights Reserved
+
+Template code for CS 4646/7646
+
+Georgia Tech asserts copyright ownership of this template and all derivative
+works, including solutions to the projects assigned in this course. Students
+and other users of this template code are advised not to share it with others
+or to make it available on publicly viewable websites including repositories
+such as github and gitlab.  This copyright statement should not be removed
+or edited.
+
+We do grant permission to share solutions privately with non-students such
+as potential employers. However, sharing with other current or future
+students of CS 7646 is prohibited and subject to being investigated as a
+GT honor code violation.
+
+-----do not edit anything above this line---
+"""
+
+import random
+import numpy as np
+
+
+class QLearner(object):
+
+    def __init__(self,
+                 num_states=100,
+                 num_actions=4,
+                 alpha=0.2,
+                 gamma=0.9,
+                 rar=0.5,
+                 radr=0.99,
+                 dyna=0,
+                 verbose=False):
+
+        self.verbose = verbose
+        self.num_actions = num_actions
+        self.num_states = num_states
+        self.s = 0
+        self.a = 0
+        self.alpha = alpha
+        self.gamma = gamma
+        self.rar = rar
+        self.radr = radr
+        self.dyna = dyna
+
+        if self.dyna > 0:
+            self.model = {}
+            self.state_action_list = []
+
+        # self.q = np.random.random((num_states, num_actions))
+        self.q = np.zeros((num_states, num_actions))
+
+    def _get_a(self, s):
+        """Get best action for state. Considers rar."""
+        if random.random() < self.rar:
+            a = random.randint(0, self.num_actions - 1)
+        else:
+            a = np.argmax(self.q[s])
+        return a
+
+    def _update_q(self, s, a, r, s_prime):
+        """Updates the Q table."""
+        q_old = self.q[s][a]
+        alpha = self.alpha
+
+        # estimate optimal future value
+        a_max = np.argmax(self.q[s_prime])
+        q_future = self.q[s_prime][a_max]
+
+        # calculate new value and update table
+        q_new = (1 - alpha) * q_old + alpha * (r + self.gamma * q_future)
+        self.q[s][a] = q_new
+
+        if self.verbose:
+            print(f"{q_old=} {q_future=} {q_new=}")
+
+    def querysetstate(self, s):
+        """
+        @summary: Update the state without updating the Q-table
+        @param s: The new state
+        @returns: The selected action
+        """
+        a = self._get_a(s)
+        if self.verbose:
+            print(f"s = {s}, a = {a}")
+        self.s = s
+        self.a = a
+        return self.a
+
+    def query(self, s_prime, r):
+        """
+        @summary: Update the Q table and return an action
+        @param s_prime: The new state
+        @param r: The reward
+        @returns: The selected action
+        """
+        self._update_q(self.s, self.a, r, s_prime)
+        a = self._get_a(s_prime)
+
+        # Update random action rate
+        self.rar = self.rar * self.radr
+
+        if self.dyna > 0:
+            self._update_model(self.s, self.a, r, s_prime)
+            self._dyna_q()
+
+        self.a = a
+        self.s = s_prime
+        return self.a
+
+    def _update_model(self, s, a, r, s_prime):
+        state_action = (s, a)
+        if not state_action in self.model:
+            self.model[state_action] = (r, s_prime)
+            self.state_action_list.append(state_action)
+
+    def _dyna_q(self):
+        for _ in range(self.dyna):
+            s, a = random.choice(self.state_action_list)
+            r, s_prime = self.model[(s, a)]
+            self._update_q(s, a, r, s_prime)
+
+    def author(self):
+        return 'felixm'
+
+
+if __name__ == "__main__":
+    q = QLearner(verbose=True, dyna=2)
+    q.querysetstate(2)
+    q.query(15, 1.00)
+    q.querysetstate(15)
+    q.query(17, 0.10)

qlearning_robot/grade_robot_qlearning.py

@@ -0,0 +1,387 @@
+"""MC3-P2: Q-learning & Dyna - grading script.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Usage:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd).  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    PYTHONPATH=ml4t:MC1-P2/jdoe7 python ml4t/mc2_p1_grading/grade_marketsim.py  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Copyright 2018, Georgia Institute of Technology (Georgia Tech)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Atlanta, Georgia 30332  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+All Rights Reserved  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Template code for CS 4646/7646  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Georgia Tech asserts copyright ownership of this template and all derivative  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+works, including solutions to the projects assigned in this course. Students  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+and other users of this template code are advised not to share it with others  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+or to make it available on publicly viewable websites including repositories  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+such as github and gitlab.  This copyright statement should not be removed  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+or edited.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+We do grant permission to share solutions privately with non-students such  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+as potential employers. However, sharing with other current or future  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+students of CS 7646 is prohibited and subject to being investigated as a  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT honor code violation.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+-----do not edit anything above this line---  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Student Name: Tucker Balch (replace with your name)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT User ID: tb34 (replace with your User ID)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT ID: 900897987 (replace with your GT ID)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+"""  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import pytest  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+from grading.grading import grader, GradeResult, run_with_timeout, IncorrectOutput  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import os  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import sys  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import traceback as tb  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import datetime as dt  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import random  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import numpy as np  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import pandas as pd  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+from collections import namedtuple  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import util  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# Student modules to import  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+main_code = "QLearner"  # module name to import  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+robot_qlearning_testing_seed=1490652871  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+QLearningTestCase = namedtuple('QLearning', ['description', 'group','world_file','best_reward','median_reward','max_time','points'])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+qlearning_test_cases = [  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 1",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world01.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-17,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-29.5,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 2",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world02.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-14,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-19,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 4",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world04.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-24,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-33,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 6",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world06.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-16,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-23.5,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 7",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world07.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-14,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-26,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 8",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world08.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-14,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-19,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 9",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world09.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-15,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-20,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 10",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='nodyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world10.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-28,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-42,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=2,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=9.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # Dyna test cases  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 1, dyna=200",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='dyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world01.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-12,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-29.5,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=10,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=2.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="World 2, dyna=200",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='dyna',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world02.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=-14,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=-19,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=10,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=2.5  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    QLearningTestCase(  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        description="Author check",  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        group='author',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world_file='world01.csv',  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        best_reward=0,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        median_reward=0,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        max_time=10,  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        points=0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ),  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+max_points = 100.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+html_pre_block = True  # surround comments with HTML <pre> tag (for T-Square comments field)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# Test functon(s)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+@pytest.mark.parametrize("description,group,world_file,best_reward,median_reward,max_time,points", qlearning_test_cases)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def test_qlearning(description, group, world_file, best_reward, median_reward, max_time, points, grader):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    points_earned = 0.0  # initialize points for this test case  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    try:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if not 'QLearner' in globals():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            import importlib  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            m = importlib.import_module('QLearner')  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            globals()['QLearner'] = m  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Unpack test case  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        world = np.array([list(map(float,s.strip().split(','))) for s in util.get_robot_world_file(world_file).readlines()])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        student_reward = None  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        student_author = None  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msgs = []  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if group=='nodyna':  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            def timeoutwrapper_nodyna():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # Note: the following will NOT be commented durring final grading  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # np.random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                learner = QLearner.QLearner(num_states=100,\
+                                            num_actions = 4, \
+                                            alpha = 0.2, \
+                                            gamma = 0.9, \
+                                            rar = 0.98, \
+                                            radr = 0.999, \
+                                            dyna = 0, \
+                                            verbose=False)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                return qltest(worldmap=world,iterations=500,max_steps=10000,learner=learner,verbose=False)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_reward = run_with_timeout(timeoutwrapper_nodyna,max_time,(),{})  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect = False  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if student_reward < 1.5*median_reward:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("   Reward too low, expected %s, found %s"%(median_reward,student_reward))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif group=='dyna':  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            def timeoutwrapper_dyna():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # Note: the following will NOT be commented durring final grading  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # np.random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                learner = QLearner.QLearner(num_states=100,\
+                                            num_actions = 4, \
+                                            alpha = 0.2, \
+                                            gamma = 0.9, \
+                                            rar = 0.5, \
+                                            radr = 0.99, \
+                                            dyna = 200, \
+                                            verbose=False)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                return qltest(worldmap=world,iterations=50,max_steps=10000,learner=learner,verbose=False)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_reward = run_with_timeout(timeoutwrapper_dyna,max_time,(),{})  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect = False  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if student_reward < 1.5*median_reward:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("   Reward too low, expected %s, found %s"%(median_reward,student_reward))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif group=='author':  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            points_earned = -20  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            def timeoutwrapper_author():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # Note: the following will NOT be commented durring final grading  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                # np.random.seed(robot_qlearning_testing_seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                learner = QLearner.QLearner(num_states=100,\
+                                            num_actions = 4, \
+                                            alpha = 0.2, \
+                                            gamma = 0.9, \
+                                            rar = 0.98, \
+                                            radr = 0.999, \
+                                            dyna = 0, \
+                                            verbose=False)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                return learner.author()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_author = run_with_timeout(timeoutwrapper_author,max_time,(),{})  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_reward = best_reward+1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect = False  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if (student_author is None) or (student_author=='tb34'):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("   author() method not implemented correctly. Found {}".format(student_author))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                points_earned = points  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if (not incorrect):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            points_earned += points  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if incorrect:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            inputs_str = "    group: {}\n" \
+                         "    world_file: {}\n"\
+                         "    median_reward: {}\n".format(group, world_file, median_reward)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            raise IncorrectOutput("Test failed on one or more output criteria.\n  Inputs:\n{}\n  Failures:\n{}".format(inputs_str, "\n".join(msgs)))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    except Exception as e:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Test result: failed  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msg = "Test case description: {}\n".format(description)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Generate a filtered stacktrace, only showing erroneous lines in student file(s)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        tb_list = tb.extract_tb(sys.exc_info()[2])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        for i in range(len(tb_list)):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            row = tb_list[i]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3])  # show only filename instead of long absolute path  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        tb_list = [row for row in tb_list if row[0] in ['QLearner.py','StrategyLearner.py']]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if tb_list:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += "Traceback:\n"  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += ''.join(tb.format_list(tb_list))  # contains newlines  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif 'grading_traceback' in dir(e):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += "Traceback:\n"  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += ''.join(tb.format_list(e.grading_traceback))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msg += "{}: {}".format(e.__class__.__name__, str(e))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Report failure result to grader, with stacktrace  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        raise  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Test result: passed (no exceptions)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def getrobotpos(data):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    R = -999  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    C = -999  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for row in range(0, data.shape[0]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        for col in range(0, data.shape[1]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if data[row,col] == 2:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                C = col  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                R = row  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    if (R+C)<0:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        print("warning: start location not defined")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return R, C  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# find where the goal is in the map  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def getgoalpos(data):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    R = -999  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    C = -999  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for row in range(0, data.shape[0]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        for col in range(0, data.shape[1]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if data[row,col] == 3:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                C = col  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                R = row  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    if (R+C)<0:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        print("warning: goal location not defined")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return (R, C)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# move the robot and report reward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def movebot(data,oldpos,a):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    randomrate = 0.20 # how often do we move randomly  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    quicksandreward = -100 # penalty for stepping on quicksand  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # decide if we're going to ignore the action and  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # choose a random one instead  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    if random.uniform(0.0, 1.0) <= randomrate: # going rogue  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        a = random.randint(0,3) # choose the random direction  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # update the test location  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    if a == 0: #north  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr = testr - 1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif a == 1: #east  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testc = testc + 1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif a == 2: #south  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr = testr + 1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif a == 3: #west  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testc = testc - 1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    reward = -1 # default reward is negative one  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # see if it is legal. if not, revert  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    if testr < 0: # off the map  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif testr >= data.shape[0]: # off the map  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif testc < 0: # off the map  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif testc >= data.shape[1]: # off the map  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif data[testr, testc] == 1: # it is an obstacle  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        testr, testc = oldpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif data[testr, testc] == 5: # it is quicksand  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        reward = quicksandreward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        data[testr, testc] = 6 # mark the event  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif data[testr, testc] == 6: # it is still quicksand  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        reward = quicksandreward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        data[testr, testc] = 6 # mark the event  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    elif data[testr, testc] == 3:  # it is the goal  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        reward = 1 # for reaching the goal  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return (testr, testc), reward #return the new, legal location  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# convert the location to a single integer  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def discretize(pos):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return pos[0]*10 + pos[1]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def qltest(worldmap, iterations, max_steps, learner, verbose):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# each iteration involves one trip to the goal  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    startpos = getrobotpos(worldmap) #find where the robot starts  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    goalpos = getgoalpos(worldmap) #find where the goal is  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # max_reward = -float('inf')  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    all_rewards = list()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for iteration in range(1,iterations+1):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        total_reward = 0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        data = worldmap.copy()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        robopos = startpos  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        state = discretize(robopos) #convert the location to a state  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        action = learner.querysetstate(state) #set the state and get first action  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        count = 0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        while (robopos != goalpos) & (count<max_steps):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #move to new location according to action and then get a new action  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            newpos, stepreward = movebot(data,robopos,action)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if newpos == goalpos:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                r = 1 # reward for reaching the goal  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                r = stepreward # negative reward for not being at the goal  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            state = discretize(newpos)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            action = learner.query(state,r)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if data[robopos] != 6:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                data[robopos] = 4 # mark where we've been for map printing  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if data[newpos] != 6:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                data[newpos] = 2 # move to new location  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            robopos = newpos # update the location  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #if verbose: time.sleep(1)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            total_reward += stepreward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            count = count + 1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if verbose and (count == max_steps):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            print("timeout")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if verbose: printmap(data)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if verbose: print(f"{iteration} {total_reward}")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # if max_reward < total_reward:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        #     max_reward = total_reward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        all_rewards.append(total_reward)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # return max_reward  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return np.median(all_rewards)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+if __name__ == "__main__":  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    pytest.main(["-s", __file__])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			

qlearning_robot/testqlearner.py

@@ -0,0 +1,223 @@
+"""
+Test a Q Learner in a navigation problem.  (c) 2015 Tucker Balch
+2016-10-20 Added "quicksand" and uncertain actions.
+
+Copyright 2018, Georgia Institute of Technology (Georgia Tech)
+Atlanta, Georgia 30332
+All Rights Reserved
+
+Template code for CS 4646/7646
+
+Georgia Tech asserts copyright ownership of this template and all derivative
+works, including solutions to the projects assigned in this course. Students
+and other users of this template code are advised not to share it with others
+or to make it available on publicly viewable websites including repositories
+such as github and gitlab.  This copyright statement should not be removed
+or edited.
+
+We do grant permission to share solutions privately with non-students such
+as potential employers. However, sharing with other current or future
+students of CS 7646 is prohibited and subject to being investigated as a
+GT honor code violation.
+
+-----do not edit anything above this line---
+
+Student Name: Tucker Balch (replace with your name)
+GT User ID: tb34 (replace with your User ID)
+GT ID: 900897987 (replace with your GT ID)
+"""
+
+import numpy as np
+import random as rand
+import time
+import math
+import QLearner as ql
+
+# print out the map
+def printmap(data):
+    print("--------------------")
+    for row in range(0, data.shape[0]):
+        for col in range(0, data.shape[1]):
+            if data[row,col] == 0: # Empty space
+                print(" ", end=' ')
+            if data[row,col] == 1: # Obstacle
+                print("O", end=' ')
+            if data[row,col] == 2: # El roboto
+                print("*", end=' ')
+            if data[row,col] == 3: # Goal
+                print("X", end=' ')
+            if data[row,col] == 4: # Trail
+                print(".", end=' ')
+            if data[row,col] == 5: # Quick sand
+                print("~", end=' ')
+            if data[row,col] == 6: # Stepped in quicksand
+                print("@", end=' ')
+        print()
+    print("--------------------")
+
+# find where the robot is in the map
+def getrobotpos(data):
+    R = -999
+    C = -999
+    for row in range(0, data.shape[0]):
+        for col in range(0, data.shape[1]):
+            if data[row,col] == 2:
+                C = col
+                R = row
+    if (R+C)<0:
+        print("warning: start location not defined")
+    return R, C
+
+# find where the goal is in the map
+def getgoalpos(data):
+    R = -999
+    C = -999
+    for row in range(0, data.shape[0]):
+        for col in range(0, data.shape[1]):
+            if data[row,col] == 3:
+                C = col
+                R = row
+    if (R+C)<0:
+        print("warning: goal location not defined")
+    return (R, C)
+
+# move the robot and report reward
+def movebot(data,oldpos,a):
+    testr, testc = oldpos
+
+    randomrate = 0.20 # how often do we move randomly
+    quicksandreward = -100 # penalty for stepping on quicksand
+
+    # decide if we're going to ignore the action and
+    # choose a random one instead
+    if rand.uniform(0.0, 1.0) <= randomrate: # going rogue
+        a = rand.randint(0,3) # choose the random direction
+
+    # update the test location
+    if a == 0: #north
+        testr = testr - 1
+    elif a == 1: #east
+        testc = testc + 1
+    elif a == 2: #south
+        testr = testr + 1
+    elif a == 3: #west
+        testc = testc - 1
+
+    reward = -1 # default reward is negative one
+    # see if it is legal. if not, revert
+    if testr < 0: # off the map
+        testr, testc = oldpos
+    elif testr >= data.shape[0]: # off the map
+        testr, testc = oldpos
+    elif testc < 0: # off the map
+        testr, testc = oldpos
+    elif testc >= data.shape[1]: # off the map
+        testr, testc = oldpos
+    elif data[testr, testc] == 1: # it is an obstacle
+        testr, testc = oldpos
+    elif data[testr, testc] == 5: # it is quicksand
+        reward = quicksandreward
+        data[testr, testc] = 6 # mark the event
+    elif data[testr, testc] == 6: # it is still quicksand
+        reward = quicksandreward
+        data[testr, testc] = 6 # mark the event
+    elif data[testr, testc] == 3:  # it is the goal
+        reward = 1 # for reaching the goal
+
+    return (testr, testc), reward #return the new, legal location
+
+# convert the location to a single integer
+def discretize(pos):
+    return pos[0]*10 + pos[1]
+
+def test(map, epochs, learner, verbose):
+# each epoch involves one trip to the goal
+    startpos = getrobotpos(map) #find where the robot starts
+    goalpos = getgoalpos(map) #find where the goal is
+    scores = np.zeros((epochs,1))
+    for epoch in range(1,epochs+1):
+        total_reward = 0
+        data = map.copy()
+        robopos = startpos
+        state = discretize(robopos) #convert the location to a state
+        action = learner.querysetstate(state) #set the state and get first action
+        count = 0
+        while (robopos != goalpos) & (count<10000):
+
+            #move to new location according to action and then get a new action
+            newpos, stepreward = movebot(data,robopos,action)
+            if newpos == goalpos:
+                r = 1 # reward for reaching the goal
+            else:
+                r = stepreward # negative reward for not being at the goal
+            state = discretize(newpos)
+            action = learner.query(state,r)
+
+            if data[robopos] != 6:
+                data[robopos] = 4 # mark where we've been for map printing
+            if data[newpos] != 6:
+                data[newpos] = 2 # move to new location
+            robopos = newpos # update the location
+            #if verbose: time.sleep(1)
+            total_reward += stepreward
+            count = count + 1
+        if count == 100000:
+            print("timeout")
+        if verbose: printmap(data)
+        if verbose: print(f"{epoch}, {total_reward}")
+        scores[epoch-1,0] = total_reward
+    return np.median(scores)
+
+# run the code to test a learner
+def test_code():
+
+    verbose = False # print lots of debug stuff if True
+
+    # read in the map
+    filename = 'testworlds/world01.csv'
+    inf = open(filename)
+    data = np.array([list(map(float,s.strip().split(','))) for s in inf.readlines()])
+    originalmap = data.copy() #make a copy so we can revert to the original map later
+
+    if verbose: printmap(data)
+
+    rand.seed(5)
+
+    ######## run non-dyna test ########
+    learner = ql.QLearner(num_states=100,\
+        num_actions = 4, \
+        alpha = 0.2, \
+        gamma = 0.9, \
+        rar = 0.98, \
+        radr = 0.999, \
+        dyna = 0, \
+        verbose=False) #initialize the learner
+    epochs = 500
+    total_reward = test(data, epochs, learner, verbose)
+    print(f"{epochs}, median total_reward {total_reward}")
+    print()
+    non_dyna_score = total_reward
+
+    ######## run dyna test ########
+    learner = ql.QLearner(num_states=100,\
+        num_actions = 4, \
+        alpha = 0.2, \
+        gamma = 0.9, \
+        rar = 0.5, \
+        radr = 0.99, \
+        dyna = 200, \
+        verbose=False) #initialize the learner
+    epochs = 50
+    data = originalmap.copy()
+    total_reward = test(data, epochs, learner, verbose)
+    print(f"{epochs}, median total_reward {total_reward}")
+    dyna_score = total_reward
+
+    print()
+    print()
+    print(f"results for {filename}")
+    print(f"non_dyna_score: {non_dyna_score}")
+    print(f"dyna_score    : {dyna_score}")
+
+if __name__=="__main__":
+    test_code()

qlearning_robot/testworlds/world01.csv

@@ -0,0 +1,10 @@
+3,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,1,1,1,1,1,0,0,0
+0,5,1,0,0,0,1,0,0,0
+0,5,1,0,0,0,1,0,0,0
+0,0,1,0,0,0,1,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,2,0,0,0,0,0

qlearning_robot/testworlds/world02.csv

@@ -0,0 +1,10 @@
+0,1,0,1,0,0,0,0,0,0
+0,1,0,1,0,0,0,0,0,0
+0,1,0,0,0,0,0,0,0,0
+0,1,0,1,1,1,1,1,1,1
+2,1,0,1,0,0,0,0,0,0
+0,1,0,1,0,0,1,0,0,3
+0,0,0,1,0,0,1,0,0,0
+0,1,0,0,0,0,1,1,1,1
+0,1,0,1,0,0,0,0,0,0
+0,0,0,1,0,0,0,0,0,0

qlearning_robot/testworlds/world03.csv

@@ -0,0 +1,10 @@
+0,0,0,1,0,0,0,1,0,3
+0,1,0,1,0,1,0,1,0,0
+0,1,0,1,0,1,0,1,0,1
+0,1,0,1,0,1,0,1,0,0
+0,1,0,1,0,1,0,1,1,0
+0,1,0,1,0,1,0,1,0,0
+0,1,0,1,0,1,0,1,0,1
+0,1,0,1,0,1,0,1,0,0
+0,1,0,1,0,1,0,1,1,0
+2,1,0,0,0,1,0,0,0,0

qlearning_robot/testworlds/world04.csv

@@ -0,0 +1,10 @@
+0,0,0,0,0,1,0,1,0,3
+0,0,0,0,0,1,0,1,0,0
+0,0,0,1,0,1,0,1,0,1
+0,0,0,1,0,1,0,1,0,0
+0,0,0,1,0,0,0,1,1,0
+2,0,0,1,1,1,0,1,0,0
+0,0,0,1,0,1,0,0,0,1
+0,0,5,0,0,1,0,1,0,0
+0,0,1,1,1,1,0,1,1,0
+0,0,0,0,0,1,0,0,0,0

qlearning_robot/testworlds/world05.csv

@@ -0,0 +1,10 @@
+0,1,0,0,0,1,0,1,0,3
+1,0,0,0,0,0,1,0,0,0
+0,0,1,1,0,1,0,1,0,1
+1,1,0,0,0,0,1,0,0,0
+0,0,0,0,0,0,0,0,1,0
+0,1,0,1,1,0,0,1,0,0
+1,0,0,0,0,1,0,0,0,1
+0,0,0,0,0,0,0,1,0,0
+1,0,1,0,0,1,0,0,1,0
+2,0,0,0,0,0,1,0,0,0

qlearning_robot/testworlds/world06.csv

@@ -0,0 +1,10 @@
+0,1,0,0,0,1,0,1,0,2
+1,0,0,0,0,0,1,0,0,0
+0,0,1,1,0,1,0,1,0,1
+1,1,0,0,0,0,1,0,0,0
+0,0,0,0,0,0,0,0,1,0
+0,1,0,1,1,0,0,1,0,0
+1,0,0,0,0,1,0,0,0,1
+0,0,0,0,0,0,0,1,0,0
+1,0,1,0,0,1,0,0,1,0
+3,0,0,0,0,0,1,0,0,0

qlearning_robot/testworlds/world07.csv

@@ -0,0 +1,10 @@
+0,0,0,0,2,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,1,1,1,1,1,0,0,0
+0,0,1,0,3,0,1,0,0,0
+0,0,1,0,0,0,1,0,0,0
+0,0,1,0,0,0,1,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0

qlearning_robot/testworlds/world08.csv

@@ -0,0 +1,10 @@
+0,1,0,1,0,0,0,0,0,0
+0,1,0,1,0,0,0,0,0,0
+0,1,0,0,0,0,0,0,0,0
+0,1,0,1,1,1,1,1,1,1
+3,1,0,1,0,0,0,0,0,0
+0,1,0,1,0,0,1,0,0,2
+0,0,0,1,0,0,1,0,0,0
+0,1,0,0,0,0,1,1,1,1
+0,1,0,1,0,0,0,0,0,0
+0,0,0,1,0,0,0,0,0,0

qlearning_robot/testworlds/world09.csv

@@ -0,0 +1,10 @@
+0,0,0,0,0,2,0,0,0,0
+0,0,0,1,0,0,1,0,0,0
+0,1,0,1,0,0,1,0,1,0
+0,1,0,1,1,1,1,0,1,0
+0,1,0,0,1,0,0,0,1,0
+0,1,1,1,1,1,1,1,1,0
+0,0,0,0,1,0,0,0,0,0
+0,0,0,0,1,0,0,1,0,0
+0,0,0,0,1,0,0,1,0,0
+0,0,0,0,1,3,0,1,0,0

qlearning_robot/testworlds/world10.csv

@@ -0,0 +1,10 @@
+0,0,0,0,0,0,0,0,0,0
+0,0,0,1,0,0,1,0,0,0
+0,1,0,1,0,0,1,0,1,0
+0,1,0,1,1,1,1,0,1,0
+0,1,0,0,1,0,0,0,1,0
+0,1,1,1,1,0,1,1,1,0
+0,0,0,0,1,0,0,0,0,0
+0,0,0,0,1,0,0,1,0,0
+0,0,0,0,1,0,0,1,0,0
+0,0,0,2,1,3,0,1,0,0

strategy_evaluation/AbstractTreeLearner.py

@@ -0,0 +1,77 @@
+import numpy as np
+
+
+class AbstractTreeLearner:
+    LEAF = -1
+    NA = -1
+
+    def author(self):
+        return 'felixm' # replace tb34 with your Georgia Tech username
+
+    def create_node(self, factor, split_value, left, right):
+        return np.array([(factor, split_value, left, right), ],
+                        dtype='|i4, f4, i4,  i4')
+
+    def query_point(self, point):
+        node_index = 0
+        while self.rel_tree[node_index][0] != self.LEAF:
+            node = self.rel_tree[node_index]
+            split_factor = node[0]
+            split_value = node[1]
+            if point[split_factor] <= split_value:
+                # Recurse into left sub-tree.
+                node_index += node[2]
+            else:
+                node_index += node[3]
+        v = self.rel_tree[node_index][1]
+        return v
+
+    def query(self, points):
+        """
+        @summary: Estimate a set of test points given the model we built.
+        @param points: should be a numpy array with each row corresponding to a specific query.
+        @returns the estimated values according to the saved model.
+        """
+        query_point = lambda p: self.query_point(p)
+        r = np.apply_along_axis(query_point, 1, points)
+        return r
+
+    def build_tree(self, xs, y):
+        """
+        @summary: Build a decision tree from the training data.
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        assert(xs.shape[0] == y.shape[0])
+        assert(xs.shape[0] > 0) # If this is 0 something went wrong.
+
+        if xs.shape[0] <= self.leaf_size:
+            value = np.mean(y)
+            if value < -0.2:
+                value = -1
+            elif value > 0.2:
+                value = 1
+            else:
+                value = 0
+            return self.create_node(self.LEAF, value, self.NA, self.NA)
+
+        if np.all(y[0] == y):
+            return self.create_node(self.LEAF, y[0], self.NA, self.NA)
+
+        i, split_value = self.get_i_and_split_value(xs, y)
+        select_l = xs[:, i] <= split_value
+        select_r = xs[:, i] > split_value
+        lt = self.build_tree(xs[select_l], y[select_l])
+        rt = self.build_tree(xs[select_r], y[select_r])
+        root = self.create_node(i, split_value, 1, lt.shape[0] + 1)
+        root = np.concatenate([root, lt, rt])
+        return root
+
+    def addEvidence(self, data_x, data_y):
+        """
+        @summary: Add training data to learner
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        self.rel_tree = self.build_tree(data_x, data_y)
+

strategy_evaluation/BenchmarkStrategy.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import util as ut
+import datetime as dt
+
+
+class BenchmarkStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    def addEvidence(self, symbol=0, sd=0, ed=0, sv=0):
+        """Keep this so that API is valid."""
+        pass
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        """Benchmark is to buy 1000 shares and hold."""
+        dates = pd.date_range(sd, ed)
+        prices = ut.get_data([symbol], dates)  # automatically adds SPY
+
+        orders = pd.DataFrame(index=prices.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        orders.iloc[0] = [symbol, "BUY", 1000]
+        orders.iloc[-1] = [symbol, "SELL", -1000]
+
+        if self.verbose:
+            print(type(orders))  # it better be a DataFrame!
+            print(orders)
+        return orders

strategy_evaluation/ManualStrategy.py

@@ -0,0 +1,114 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+
+
+class ManualStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    # this method should create a QLearner, and train it for trading
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        # add your code to do learning here
+
+        # example usage of the old backward compatible util function
+        syms = [symbol]
+        dates = pd.date_range(sd, ed)
+        prices_all = util.get_data(syms, dates)  # automatically adds SPY
+        prices = prices_all[syms]  # only portfolio symbols
+        # prices_SPY = prices_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(prices)
+
+        # example use with new colname
+        # automatically adds SPY
+        volume_all = util.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    def macd_strat(self, macd, orders):
+        """Strategy based on MACD cross."""
+
+        def strat(ser):
+            m = macd.loc[ser.index]
+            prev_macd, prev_signal, _ = m.iloc[0]
+            cur_macd, cur_signal, _ = m.iloc[1]
+            shares = 0
+            if cur_macd < -1 and prev_macd < prev_signal \
+                             and cur_macd > cur_signal:
+                if self.holding == 0:
+                    shares = 1000
+                elif self.holding == -1000:
+                    shares = 2000
+            elif cur_macd > 1 and prev_macd > prev_signal \
+                              and cur_macd < cur_signal:
+                if self.holding == 0:
+                    shares = -1000
+                elif self.holding == 1000:
+                    shares = -2000
+            self.holding += shares
+            return shares
+
+        orders['Shares'] = orders['Shares'].rolling(2).apply(strat)
+
+    def three_indicator_strat(self, macd, rsi, price_sma, orders):
+        """Strategy based on three indicators. Thresholds selected based on
+        scatter plots."""
+        def strat(row):
+            shares = 0
+            _, _, macd_diff = macd.loc[row.name]
+            cur_rsi = rsi.loc[row.name][0]
+            cur_price_sma = price_sma.loc[row.name][0]
+            if self.holding == -1000 and cur_price_sma < 0.9:
+                shares = 2000
+            elif self.holding == 0 and cur_price_sma < 0.9:
+                shares = 1000
+            elif self.holding == -1000 and cur_rsi > 80:
+                shares = 2000
+            elif self.holding == 0 and cur_rsi > 80:
+                shares = 1000
+            elif self.holding == -1000 and macd_diff < -0.5:
+                shares = 2000
+            elif self.holding == 0 and macd_diff < -0.5:
+                shares = 1000
+            elif self.holding == 1000 and cur_price_sma > 1.1:
+                shares = -2000
+            elif self.holding == 0 and cur_price_sma > 1.1:
+                shares = -1000
+            self.holding += shares
+            return shares
+
+        orders['Shares'] = orders.apply(strat, axis=1)
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000, macd_strat=False):
+
+        self.holding = 0
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        df.drop(columns=["SPY"], inplace=True)
+
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+
+        macd = indicators.macd(df, symbol)
+        rsi = indicators.rsi(df, symbol)
+        price_sma = indicators.price_sma(df, symbol, [8])
+
+        if macd_strat:
+            self.macd_strat(macd, orders)
+        else:
+            self.three_indicator_strat(macd, rsi, price_sma, orders)
+        return orders

strategy_evaluation/QLearner.py

@@ -0,0 +1,169 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+from qlearning_robot.QLearner import QLearner as Learner
+from dataclasses import dataclass
+
+@dataclass
+class Holding:
+    cash: int
+    shares: int
+    equity: int
+
+
+class QLearner(object):
+
+    def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False, n_bins=5):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.testing = testing  # Decides which type of order df to return.
+        self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
+        self.n_bins = n_bins
+        self.bins = {}
+        self.num_states = self.get_num_states()
+        self.num_actions = 3  # buy, sell, hold
+        self.learner = Learner(self.num_states, self.num_actions)
+
+    def row_to_state(self, holding, df_row):
+        """Transforms a row into a state value."""
+        holding = (holding + 1000) // 1000
+        assert(holding in [0, 1, 2])
+
+        # For each indicator that goes into the state the interval becomes
+        # smaller based on how many bins the indicator has.  The first
+        # 'indicator' is the information about how many shares we are currently
+        # holding. So for example, if I have 450 states then the intervall (aka
+        # remaining_states) is 150 because there are three values for holding:
+        #   holding = 0 -> state = 0 * 150 = 0
+        #   holding = 1 -> state = 1 * 150 = 150
+        #   holding = 2 -> state = 2 * 150 = 300
+        remaining_states = self.num_states // 3
+        state = holding * remaining_states
+
+        for indicator in self.indicators:
+            value = df_row[indicator]
+            bin_n = self.indicator_value_to_bin(indicator, value)
+            remaining_states //= self.n_bins
+            state += bin_n * remaining_states
+        return state
+
+    def indicator_value_to_bin(self, indicator, value):
+        for i, upper_bound in enumerate(self.bins[indicator]):
+            if value < upper_bound:
+                return i
+        return i + 1
+
+    def add_indicators(self, df, symbol):
+        """Add indicators for learning to DataFrame."""
+        for indicator in self.indicators:
+            if indicator == "macd_diff":
+                indicators.macd(df, symbol)
+                df.drop(columns=["macd", "macd_signal"], inplace=True)
+            elif indicator == "rsi":
+                indicators.rsi(df, symbol)
+            elif indicator.startswith("price_sma_"):
+                period = int(indicator.replace("price_sma_", ""))
+                indicators.price_sma(df, symbol, [period])
+        df.drop(columns=["SPY"], inplace=True)
+        df.dropna(inplace=True)
+
+    def bin_indicators(self, df):
+        """Create bins for indicators."""
+        for indicator in self.indicators:
+            ser, bins = pd.qcut(df[indicator], self.n_bins, retbins=True)
+            self.bins[indicator] = bins[1:self.n_bins]
+
+    def get_num_states(self):
+        """Return the total num of states."""
+        num_states = 3  # Three states holding (1000, 0, -1000)
+        for _ in self.indicators:
+            num_states *= self.n_bins
+        return num_states
+
+    def handle_order(self, action, holding, adj_closing_price):
+        shares = 0
+        if action == 0:  # buy
+            if holding.shares == 0 or holding.shares == -1000:
+                shares = 1000
+        elif action == 1:  # sell
+            if holding.shares== 0 or holding.shares == 1000:
+                shares = -1000
+        elif action == 2:  # hold
+            shares = 0
+
+        cost = shares * adj_closing_price
+        if shares != 0:
+            # Charge commission and deduct impact penalty
+            holding.cash -= self.commission
+            holding.cash -= (self.impact * adj_closing_price * abs(shares))
+            holding.cash -= cost
+            holding.shares += shares
+
+        holding.equity = holding.cash + holding.shares * adj_closing_price
+
+    def get_reward(self, equity, new_equity):
+        if new_equity > equity:
+            return 1
+        return -1
+
+    def train(self, df, symbol, sv):
+        holding = Holding(sv, 0, sv)
+
+        row = df.iloc[0]
+        state = self.row_to_state(holding.shares, row)
+        action = self.learner.querysetstate(state)
+        adj_closing_price = row[symbol]
+        equity = holding.equity
+        self.handle_order(action, holding, adj_closing_price)
+
+        for index, row in df.iloc[1:].iterrows():
+            adj_closing_price = row[symbol]
+            new_equity = holding.cash + holding.shares * adj_closing_price
+            r = self.get_reward(equity, new_equity)
+            s_prime = self.row_to_state(holding.shares, row)
+            a = self.learner.query(s_prime, r)
+            equity = new_equity
+            self.handle_order(a, holding, adj_closing_price)
+            if self.verbose:
+                print(f"{holding=} {s_prime=} {r=} {a=}")
+
+    def addEvidence(self, symbol="IBM", sd=dt.datetime(2008, 1, 1), ed=dt.datetime(2009, 1, 1), sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self.add_indicators(df, symbol)
+        self.bin_indicators(df)
+
+        for _ in range(15):
+            self.train(df, symbol, sv)
+
+    def testPolicy(self, symbol="IBM", sd=dt.datetime(2009, 1, 1), ed=dt.datetime(2010, 1, 1), sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        shares = orders["Shares"]
+        self.add_indicators(df, symbol)
+        holding = 0
+
+        for index, row in df.iterrows():
+            state = self.row_to_state(holding, row)
+            action = self.learner.querysetstate(state)
+
+            if action == 0:  # buy
+                if holding == 0 or holding == -1000:
+                    holding += 1000
+                    orders.loc[index, "Shares"] = 1000
+            elif action == 1:  # sell
+                if holding == 0 or holding == 1000:
+                    holding -= 1000
+                    orders.loc[index, "Shares"] = -1000
+            elif action == 2:  # hold
+                pass
+
+        if self.testing:
+            return orders
+        else:
+            return orders[["Shares"]]
+

strategy_evaluation/RTLearner.py

@@ -0,0 +1,30 @@
+import numpy as np
+from AbstractTreeLearner import AbstractTreeLearner
+
+
+class RTLearner(AbstractTreeLearner):
+
+    def __init__(self, leaf_size = 1, verbose = False):
+        self.leaf_size = leaf_size
+        self.verbose = verbose
+
+    def get_i_and_split_value(self, xs, y):
+        """
+        @summary: Pick a random i and split value.
+
+        Make sure that not all X are the same for i and also pick
+        different values to average the split_value from.
+        """
+        i = np.random.randint(0, xs.shape[1])
+        while np.all(xs[0,i] == xs[:,i]):
+            i = np.random.randint(0, xs.shape[1])
+
+        # I don't know about the performance of this, but at least it
+        # terminates reliably. If the two elements are the same something is
+        # wrong.
+        a = np.array(list(set(xs[:, i])))
+        r1, r2 = np.random.choice(a, size = 2, replace = False)
+        assert(r1 != r2)
+        split_value = (r1 + r2) / 2.0
+        return i, split_value
+

strategy_evaluation/StrategyLearner.py

@@ -0,0 +1,106 @@
+import datetime as dt
+import pandas as pd
+import util
+import indicators
+from RTLearner import RTLearner
+
+
+class StrategyLearner(object):
+
+    def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.testing = testing
+
+    def _get_volume(self):
+        """For reference."""
+        volume_all = ut.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    def _add_indicators(self, df, symbol):
+        """Add indicators for learning to DataFrame."""
+        df.drop(columns=["SPY"], inplace=True)
+        indicators.macd(df, symbol)
+        indicators.rsi(df, symbol)
+        indicators.price_sma(df, symbol, [8])
+        indicators.price_delta(df, symbol, 3)
+        df.dropna(inplace=True)
+
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        self.y_threshold = 0.2
+        self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+
+        def classify_y(row):
+            if row > self.y_threshold:
+                return 1
+            elif row < -self.y_threshold:
+                return -1
+            else:
+                pass
+            return 0
+
+        def set_y_threshold(pct):
+            if max(pct) < 0.2:
+                self.y_threshold = 0.02
+
+        self.learner = RTLearner(leaf_size = 5)
+        # self.learner = BagLearner(RTLearner, 3, {'leaf_size': 5})
+        data_x = df[self.indicators].to_numpy()
+        pct = df['pct_3']
+
+        # This is a hack to get a low enough buy/sell threshold for the
+        # cyclic the test 'ML4T-220' where the max pct_3 is 0.0268.
+        set_y_threshold(pct)
+        y = pct.apply(classify_y)
+
+        self.learner.addEvidence(data_x, y.to_numpy())
+        return y
+
+    def strat(self, data_y, orders):
+        self.holding = 0
+
+        def strat(row):
+            y = int(data_y.loc[row.name][0])
+            shares = 0
+            if self.holding == 0 and y == 1:
+                shares = 1000
+            elif self.holding == -1000 and y == 1:
+                shares = 2000
+            elif self.holding == 0 and y == -1:
+                shares = -1000
+            elif self.holding == 1000 and y == -1:
+                shares = -2000
+            self.holding += shares
+            return shares
+
+        orders["Shares"] = orders.apply(strat, axis=1)
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+        data_x = df[self.indicators].to_numpy()
+        data_y = pd.DataFrame(index=df.index, data=self.learner.query(data_x))
+
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        self.strat(data_y, orders)
+        if self.testing:
+            return orders
+        else:
+            return orders[["Shares"]]
+

strategy_evaluation/experiment1.py

@@ -0,0 +1,236 @@
+import pandas as pd
+import datetime as dt
+import sys
+
+import util
+import indicators
+import marketsim.marketsim as marketsim
+import matplotlib.pyplot as plt
+from matplotlib.widgets import MultiCursor
+from BenchmarkStrategy import BenchmarkStrategy
+from ManualStrategy import ManualStrategy
+from StrategyLearner import StrategyLearner
+from QLearner import QLearner
+
+
+def plot_indicators(symbol, df):
+    fig, ax = plt.subplots(4, sharex=True)
+
+    price_sma = indicators.price_sma(df, symbol, [8])
+    bb = indicators.bollinger_band(df, symbol)
+    rsi = indicators.rsi(df, symbol)
+    macd = indicators.macd(df, symbol).copy()
+
+    df[[symbol]].plot(ax=ax[0])
+    bb.plot(ax=ax[0])
+    price_sma.plot(ax=ax[1])
+    macd.plot(ax=ax[2])
+    rsi.plot(ax=ax[3])
+    for a in ax.flat:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    plt.show()
+    sys.exit(0)
+
+
+def visualize_correlations(symbol, df):
+    indicators.price_sma(df, symbol, [8, 21])
+    indicators.price_delta(df, symbol, 5)
+    indicators.price_delta(df, symbol, 3)
+    indicators.price_delta(df, symbol, 1)
+    indicators.macd(df, symbol)
+    indicators.rsi(df, symbol)
+
+    # df = df[df['rsi'] > 80]
+    fig, ax = plt.subplots(3, 2)  # sharex=True)
+    df.plot.scatter(x="price_sma_8", y="pct_5", ax=ax[0, 0])
+    df.plot.scatter(x="price_sma_8", y="pct_3", ax=ax[1, 0])
+    df.plot.scatter(x="price_sma_8", y="pct_1", ax=ax[2, 0])
+    # df.plot.scatter(x="rsi", y="pct_5", ax=ax[0, 1])
+    # df.plot.scatter(x="rsi", y="pct_3", ax=ax[1, 1])
+    # df.plot.scatter(x="rsi", y="pct_1", ax=ax[2, 1])
+    df.plot.scatter(x="macd_diff", y="pct_5", ax=ax[0, 1])
+    df.plot.scatter(x="macd_diff", y="pct_3", ax=ax[1, 1])
+    df.plot.scatter(x="macd_diff", y="pct_1", ax=ax[2, 1])
+
+    for a in ax.flat:
+        a.grid()
+    plt.show()
+    sys.exit(0)
+
+
+def compare_manual_strategies(symbol, sv, sd, ed):
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, sv)
+    df["Benchmark"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, sv, macd_strat=True)
+    df["MACD Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders MACD"] = orders["Shares"]
+    # df["Holding Manual"] = orders["Shares"].cumsum()
+
+    orders = ms.testPolicy(symbol, sd, ed, sv)
+    df["Three Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Three"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "MACD Strat", "Three Strat"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders MACD", "Orders Three"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+
+    # plt.show()
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_1.png', dpi=fig.dpi)
+
+
+def compare_all_strategies(symbol, sv, sd, ed):
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    normalize = indicators.normalize
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, sv)
+    df["Benchmark"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, sv)
+    df["Manual"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Manual"] = orders["Shares"]
+
+    sl = StrategyLearner(testing=True)
+    sl.addEvidence(symbol, sd, ed, sv)
+    orders = sl.testPolicy(symbol, sd, ed, sv)
+    df["Strategy"] = normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Strategy"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "Manual", "Strategy"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders Manual", "Orders Strategy"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+
+    # plt.show()
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_2.png', dpi=fig.dpi)
+
+
+def compare_number_trades():
+    symbol = "JPM"
+    sv = 10000
+    sd = dt.datetime(2008, 1, 1)  # in-sample
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+
+    print(f"| commission | n_orders |")
+    print(f"-------------------------")
+    for commission in [0, 9.95, 20, 50, 100]:
+        ql = QLearner(testing=True, commission=commission, impact=0.005)
+        ql.addEvidence(symbol, sd, ed, sv)
+        orders = ql.testPolicy(symbol, sd, ed, sv)
+        n_orders = orders[orders["Shares"] != 0].shape[0]
+        print(f"| {commission} | {n_orders} |")
+
+def compare_q_learners():
+    symbol = "JPM"
+    sv = 10000
+    sd = dt.datetime(2008, 1, 1)  # in-sample
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+    sd_out = dt.datetime(2010, 1, 1)  # out-sample
+    ed_out = dt.datetime(2011, 12, 31)  # out-sample
+
+    df = util.get_data([symbol], pd.date_range(sd_out, ed_out))
+    df.drop(columns=["SPY"], inplace=True)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
+    df["Benchmark"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL 5"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL 5"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False, n_bins=4)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL 4"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL 4"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "QL 5", "QL 4"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders QL 5", "Orders QL 4"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_4.png', dpi=fig.dpi)
+    sys.exit(0)
+
+
+def experiment1(create_report=False):
+    symbol = "JPM"
+    sv = 10000
+    sd = dt.datetime(2008, 1, 1)  # in-sample
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+    sd_out = dt.datetime(2010, 1, 1)  # out-sample
+    ed_out = dt.datetime(2011, 12, 31)  # out-sample
+
+    df = util.get_data([symbol], pd.date_range(sd_out, ed_out))
+    df.drop(columns=["SPY"], inplace=True)
+
+    if create_report:
+        compare_manual_strategies(symbol, sv, sd, ed)
+        compare_all_strategies(symbol, sv, sd, ed)
+        sys.exit(0)
+
+    # visualize_correlations(symbol, df)
+    # plot_indicators(symbol, df)
+    # compare_number_trades(symbol, sv, sd, ed)
+    # compare_q_learners()
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
+    df["Benchmark"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ql = QLearner(testing=True, verbose=False)
+    ql.addEvidence(symbol, sd, ed, sv)
+    orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
+    df["QL"] = indicators.normalize(marketsim.compute_portvals(orders, sv))
+    df["Orders QL"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "QL"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders QL"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    plt.show()
+    # fig.set_size_inches(10, 8, forward=True)
+    # plt.savefig('figure_4.png', dpi=fig.dpi)
+
+
+if __name__ == "__main__":
+    experiment1()

strategy_evaluation/experiment2.py

@@ -0,0 +1,8 @@
+import experiment1
+
+def experiment2():
+    experiment1.compare_number_trades()
+
+
+if __name__ == "__main__":
+    experiment2()

strategy_evaluation/grade_strategy_learner.py

@@ -0,0 +1,339 @@
+"""MC3-P3: Strategy Learner - grading script.
+
+Usage:
+- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd).
+- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.:
+    PYTHONPATH=ml4t:MC1-P2/jdoe7 python ml4t/mc2_p1_grading/grade_marketsim.py
+
+Copyright 2017, Georgia Tech Research Corporation
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+Template code for CS 4646/7646
+
+Georgia Tech asserts copyright ownership of this template and all derivative
+works, including solutions to the projects assigned in this course. Students
+and other users of this template code are advised not to share it with others
+or to make it available on publicly viewable websites including repositories
+such as github and gitlab.  This copyright statement should not be removed
+or edited.
+
+We do grant permission to share solutions privately with non-students such
+as potential employers. However, sharing with other current or future
+students of CS 7646 is prohibited and subject to being investigated as a
+GT honor code violation.
+
+-----do not edit anything above this line---
+
+Student Name: Tucker Balch (replace with your name)
+GT User ID: tb34 (replace with your User ID)
+GT ID: 900897987 (replace with your GT ID)
+"""
+
+import pytest
+from grading.grading import grader, GradeResult, run_with_timeout, IncorrectOutput
+
+import os
+import sys
+import traceback as tb
+
+import datetime as dt
+import numpy as np
+import pandas as pd
+from collections import namedtuple
+
+import time
+import util
+import random
+
+# Test cases
+StrategyTestCase = namedtuple('Strategy', ['description','insample_args','outsample_args','benchmark_type','benchmark','impact','train_time','test_time','max_time','seed'])
+strategy_test_cases = [
+    StrategyTestCase(
+        description="ML4T-220",
+        insample_args=dict(symbol="ML4T-220",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="ML4T-220",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='clean',
+        benchmark=1.0, #benchmark updated Apr 24 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="AAPL",
+        insample_args=dict(symbol="AAPL",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="AAPL",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='stock',
+        benchmark=0.1581999999999999, #benchmark computed Nov 22 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="SINE_FAST_NOISE",
+        insample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='noisy',
+        benchmark=2.0, #benchmark updated Apr 24 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+    StrategyTestCase(
+        description="UNH - In sample",
+        insample_args=dict(symbol="UNH",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
+        outsample_args=dict(symbol="UNH",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
+        benchmark_type='stock',
+        benchmark= -0.25239999999999996, #benchmark computed Nov 22 2017
+        impact=0.0,
+        train_time=25,
+        test_time=5,
+        max_time=60,
+        seed=1481090000
+        ),
+]
+
+max_points = 60.0
+html_pre_block = True  # surround comments with HTML <pre> tag (for T-Square comments field)
+
+MAX_HOLDINGS = 1000
+
+# Test functon(s)
+@pytest.mark.parametrize("description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed", strategy_test_cases)
+def test_strategy(description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed, grader):
+    """Test StrategyLearner.
+
+    Requires test description, insample args (dict), outsample args (dict), benchmark_type (str), benchmark (float)
+    max time (seconds), points for this test case (int), random seed (long), and a grader fixture.
+    """
+    points_earned = 0.0  # initialize points for this test case
+    try:
+        incorrect = True
+        if not 'StrategyLearner' in globals():
+            import importlib
+            m = importlib.import_module('StrategyLearner')
+            globals()['StrategyLearner'] = m
+        outsample_cr_to_beat = None
+        if benchmark_type == 'clean':
+            outsample_cr_to_beat = benchmark
+        def timeoutwrapper_strategylearner():
+            #Set fixed seed for repetability
+            np.random.seed(seed)
+            random.seed(seed)
+            learner = StrategyLearner.StrategyLearner(verbose=False,impact=impact)
+            tmp = time.time()
+            learner.addEvidence(**insample_args)
+            train_t = time.time()-tmp
+            tmp = time.time()
+            insample_trades_1 = learner.testPolicy(**insample_args)
+            test_t = time.time()-tmp
+            insample_trades_2 = learner.testPolicy(**insample_args)
+            tmp = time.time()
+            outsample_trades = learner.testPolicy(**outsample_args)
+            out_test_t = time.time()-tmp
+            return insample_trades_1, insample_trades_2, outsample_trades, train_t, test_t, out_test_t
+        msgs = []
+        in_trades_1, in_trades_2, out_trades, train_t, test_t, out_test_t = run_with_timeout(timeoutwrapper_strategylearner,max_time,(),{})
+        incorrect = False
+        if len(in_trades_1.shape)!=2 or in_trades_1.shape[1]!=1:
+            incorrect=True
+            msgs.append("  First insample trades DF has invalid shape: {}".format(in_trades_1.shape))
+        elif len(in_trades_2.shape)!=2 or in_trades_2.shape[1]!=1:
+            incorrect=True
+            msgs.append("  Second insample trades DF has invalid shape: {}".format(in_trades_2.shape))
+        elif len(out_trades.shape)!=2 or out_trades.shape[1]!=1:
+            incorrect=True
+            msgs.append("  Out-of-sample trades DF has invalid shape: {}".format(out_trades.shape))
+        else:
+            tmp_csum=0.0
+            for date,trade in in_trades_1.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in first insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in first insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            tmp_csum=0.0
+            for date,trade in in_trades_2.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in second insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in second insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            tmp_csum=0.0
+            for date,trade in out_trades.iterrows():
+                tmp_csum+= trade.iloc[0]
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):
+                   incorrect=True
+                   msgs.append("  illegal trade in out-of-sample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
+                   break
+                elif abs(tmp_csum)>MAX_HOLDINGS:
+                    incorrect=True
+                    msgs.append("  holdings more than {} long or short in out-of-sample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
+                    break
+            # if (((in_trades_1.abs()!=0) & (in_trades_1.abs()!=MAX_HOLDINGS) & (in_trades_1.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((in_trades_2.abs()!=0) & (in_trades_2.abs()!=MAX_HOLDINGS) & (in_trades_2.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((out_trades.abs()!=0)  & (out_trades.abs()!=MAX_HOLDINGS)  & (out_trades.abs()!=2*MAX_HOLDINGS)).any().any()):
+            #     incorrect = True
+            #     msgs.append("  illegal trade. abs(trades) not one of ({},{},{})".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS))
+            # if ((in_trades_1.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((in_trades_2.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((out_trades.cumsum().abs()>MAX_HOLDINGS).any()[0]):
+            #     incorrect = True
+            #     msgs.append("  holdings more than {} long or short".format(MAX_HOLDINGS))
+        if not(incorrect):
+            if train_t>train_time:
+                incorrect=True
+                msgs.append("  addEvidence() took {} seconds, max allowed {}".format(train_t,train_time))
+            else:
+                points_earned += 1.0
+            if test_t > test_time:
+                incorrect = True
+                msgs.append("  testPolicy() took {} seconds, max allowed {}".format(test_t,test_time))
+            else:
+                points_earned += 2.0
+            if not((in_trades_1 == in_trades_2).all()[0]):
+                incorrect = True
+                mismatches = in_trades_1.join(in_trades_2,how='outer',lsuffix='1',rsuffix='2')
+                mismatches = mismatches[mismatches.iloc[:,0]!=mismatches.iloc[:,1]]
+                msgs.append("  consecutive calls to testPolicy() with same input did not produce same output:")
+                msgs.append("  Mismatched trades:\n {}".format(mismatches))
+            else:
+                points_earned += 2.0
+            student_insample_cr = evalPolicy2(insample_args['symbol'],in_trades_1,insample_args['sv'],insample_args['sd'],insample_args['ed'],market_impact=impact,commission_cost=0.0)
+            student_outsample_cr = evalPolicy2(outsample_args['symbol'],out_trades, outsample_args['sv'],outsample_args['sd'],outsample_args['ed'],market_impact=impact,commission_cost=0.0)
+            if student_insample_cr <= benchmark:
+                incorrect = True
+                msgs.append("  in-sample return ({}) did not beat benchmark ({})".format(student_insample_cr,benchmark))
+            else:
+                points_earned += 5.0
+            if outsample_cr_to_beat is None:
+                if out_test_t > test_time:
+                    incorrect = True
+                    msgs.append("  out-sample took {} seconds, max of {}".format(out_test_t,test_time))
+                else:
+                    points_earned += 5.0
+            else:
+                if student_outsample_cr < outsample_cr_to_beat:
+                    incorrect = True
+                    msgs.append("  out-sample return ({}) did not beat benchmark ({})".format(student_outsample_cr,outsample_cr_to_beat))
+                else:
+                    points_earned += 5.0
+        if incorrect:
+            inputs_str = "    insample_args: {}\n" \
+                         "    outsample_args: {}\n" \
+                         "    benchmark_type: {}\n" \
+                         "    benchmark: {}\n" \
+                         "    train_time: {}\n" \
+                         "    test_time: {}\n" \
+                         "    max_time: {}\n" \
+                         "    seed: {}\n".format(insample_args, outsample_args, benchmark_type, benchmark, train_time, test_time, max_time,seed)
+            raise IncorrectOutput("Test failed on one or more output criteria.\n  Inputs:\n{}\n  Failures:\n{}".format(inputs_str, "\n".join(msgs)))
+    except Exception as e:
+        # Test result: failed
+        msg = "Test case description: {}\n".format(description)
+
+        # Generate a filtered stacktrace, only showing erroneous lines in student file(s)
+        tb_list = tb.extract_tb(sys.exc_info()[2])
+        for i in range(len(tb_list)):
+            row = tb_list[i]
+            tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3])  # show only filename instead of long absolute path
+        # tb_list = [row for row in tb_list if row[0] in ['QLearner.py','StrategyLearner.py']]
+        if tb_list:
+            msg += "Traceback:\n"
+            msg += ''.join(tb.format_list(tb_list))  # contains newlines
+        elif 'grading_traceback' in dir(e):
+            msg += "Traceback:\n"
+            msg += ''.join(tb.format_list(e.grading_traceback))
+        msg += "{}: {}".format(e.__class__.__name__, str(e))
+
+        # Report failure result to grader, with stacktrace
+        grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))
+        raise
+    else:
+        # Test result: passed (no exceptions)
+        grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))
+
+def compute_benchmark(sd,ed,sv,symbol,market_impact,commission_cost,max_holdings):
+    date_idx = util.get_data([symbol,],pd.date_range(sd,ed)).index
+    orders = pd.DataFrame(index=date_idx)
+    orders['orders'] = 0; orders['orders'][0] = max_holdings; orders['orders'][-1] = -max_holdings
+    return evalPolicy2(symbol,orders,sv,sd,ed,market_impact,commission_cost)
+
+def evalPolicy(student_trades,sym_prices,startval):
+    ending_cash = startval - student_trades.mul(sym_prices,axis=0).sum()
+    ending_stocks = student_trades.sum()*sym_prices.iloc[-1]
+    return float((ending_cash+ending_stocks)/startval)-1.0
+
+def evalPolicy2(symbol, student_trades, startval, sd, ed, market_impact,commission_cost):
+    orders_df = pd.DataFrame(columns=['Shares','Order','Symbol'])
+    for row_idx in student_trades.index:
+        nshares = student_trades.loc[row_idx][0]
+        if nshares == 0:
+            continue
+        order = 'sell' if nshares < 0 else 'buy'
+        new_row = pd.DataFrame([[abs(nshares),order,symbol],],columns=['Shares','Order','Symbol'],index=[row_idx,])
+        orders_df = orders_df.append(new_row)
+    portvals = compute_portvals(orders_df, sd, ed, startval,market_impact,commission_cost)
+    return float(portvals[-1]/portvals[0])-1
+
+def compute_portvals(orders_df, start_date, end_date, startval, market_impact=0.0, commission_cost=0.0):
+    """Simulate the market for the given date range and orders file."""
+    symbols = []
+    orders = []
+    orders_df = orders_df.sort_index()
+    for date, order in orders_df.iterrows():
+        shares = order['Shares']
+        action = order['Order']
+        symbol = order['Symbol']
+        if action.lower() == 'sell':
+            shares *= -1
+        order = (date, symbol, shares)
+        orders.append(order)
+        symbols.append(symbol)
+    symbols = list(set(symbols))
+    dates = pd.date_range(start_date, end_date)
+    prices_all = util.get_data(symbols, dates)
+    prices = prices_all[symbols]
+    prices = prices.fillna(method='ffill').fillna(method='bfill')
+    prices['_CASH'] = 1.0
+    trades = pd.DataFrame(index=prices.index, columns=symbols)
+    trades = trades.fillna(0)
+    cash = pd.Series(index=prices.index)
+    cash = cash.fillna(0)
+    cash.iloc[0] = startval
+    for date, symbol, shares in orders:
+        price = prices[symbol][date]
+        val = shares * price
+        # transaction cost model
+        val += commission_cost + (pd.np.abs(shares)*price*market_impact)
+        positions = prices.loc[date] * trades.sum()
+        totalcash = cash.sum()
+        if (date < prices.index.min()) or (date > prices.index.max()):
+            continue
+        trades[symbol][date] += shares
+        cash[date] -= val
+    trades['_CASH'] = cash
+    holdings = trades.cumsum()
+    df_portvals = (prices * holdings).sum(axis=1)
+    return df_portvals
+
+if __name__ == "__main__":
+    pytest.main(["-s", __file__])

strategy_evaluation/indicators.py

@@ -0,0 +1,140 @@
+import pandas as pd
+import datetime as dt
+import matplotlib.pyplot as plt
+from util import get_data
+
+
+def author():
+    return "felixm"
+
+
+def normalize(timeseries):
+    return timeseries / timeseries.iloc[0]
+
+
+def bollinger_band(df, symbol, period=20, m=2):
+    boll_sma = df[symbol].rolling(period).mean()
+    std = df[symbol].rolling(period).std()
+    boll_up = boll_sma + m * std
+    boll_lo = boll_sma - m * std
+    key_sma, key_up, key_lo = "boll_sma", "boll_up", "boll_lo"
+    df[key_sma] = boll_sma
+    df[key_up] = boll_up
+    df[key_lo] = boll_lo
+    return df[[key_sma, key_up, key_lo]]
+
+
+def sma(df, symbol, period):
+    """Adds SMA for one or multiple periods to df and returns SMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"sma_{p}"
+        df[key] = df[symbol].rolling(p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def ema(df, symbol, period):
+    """Adds EMA for one or multiple periods to df and returns EMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"ema_{p}"
+        df[key] = df[symbol].ewm(span=p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def price_sma(df, symbol, period):
+    """Calculates SMA and adds new column price divided by SMA to the df."""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"price_sma_{p}"
+        sma = df[symbol].rolling(p).mean()
+        df[key] = df[symbol] / sma
+        keys.append(key)
+    return df[keys]
+
+
+def rsi(df, symbol, period=14):
+    """Calculates relative strength index over given period."""
+
+    def rsi(x):
+        pct = x.pct_change()
+        avg_gain = pct[pct > 0].mean()
+        avg_loss = pct[pct <= 0].abs().mean()
+        rsi = 100 - (100 /
+                     (1 + ((avg_gain / period) /
+                           (avg_loss / period))))
+        return rsi
+
+    key = "rsi"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+    return df[[key]]
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    k1 = "macd"
+    k2 = "macd_signal"
+    k3 = "macd_diff"
+    df[k1] = macd
+    df[k2] = macd.rolling(9).mean()
+    df[k3] = df[k1] - df[k2]
+    return df[[k1, k2, k3]]
+
+
+def price_delta(df, symbol, period=1):
+    """Calculate percentage change for period."""
+    k = f"pct_{period}"
+    df[k] = df[symbol].pct_change(periods=period)
+    df[k] = df[k].shift(-period)
+    return df[k]
+
+
+def test_indicators():
+    symbol = "JPM"
+
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    df = get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    df_orig = df.copy()
+    # df = normalize(df)
+
+    sma(df, symbol, 21)
+    ema(df, symbol, 21)
+    df.plot(title="21 SMA and EMA")
+    plt.savefig('figure_1.png')
+
+    df = df_orig.copy()
+    sma(df, symbol, 8)
+    price_sma(df, symbol, 8)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
+
+    df = df_orig.copy()
+    bollinger_band(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
+
+    df = df_orig.copy()
+    rsi(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df["JPM-rsi(14)"].plot(ax=axes[1], title="RSI")
+    plt.savefig('figure_4.png')
+
+    df = df_orig.copy()
+    macd(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df[["JPM-macd", "JPM-macd-signal"]].plot(ax=axes[1])
+    plt.savefig('figure_5.png')

strategy_evaluation/strategy_evaluation.md

@@ -0,0 +1,75 @@
+This document is the final report for the machine learning for trading
+course. I have implemented two manual strategies, a random tree
+learner-based strategy and one based on Q-learning.
+
+# Experiment 1
+
+I have implemented two manual strategies. The first strategy buys on a
+bullish MACD cross with a MACD smaller than zero and sells on a bearish
+MACD cross with a MACD greater than one.
+
+The second strategy uses MACD diff (the difference between the MACD and
+the MACD signal), RSI, and price SMA with a period of eight. I have
+plotted the metrics over their one, three, and five days return to find
+reasonable thresholds for the strategy.
+
+![Scatter plot to find reasonable thresholds.](figure_3.png)
+
+Based on the scatter plots, I have created a list of buy and sell
+signals. Each signal uses the current number of shares owned and one of
+the three indicators. The following figure shows the result for both
+manual strategies compared to the benchmark. Both approaches do well in
+the in-sample period but worse afterward, which I expected because I
+cherry-picked the thresholds based on the in-sample period's scatter
+plots.
+
+![First strategy based on MACD. Better than just holding.](figure_1.png)
+
+Next, I have implemented a random tree-based strategy learner. The
+learner uses a leaf size of five and no bagging. A smaller leaf size
+would result in overfitting to the in-sample data. But as the following
+screenshot shows, five works well, and the RT learner does well for the
+out of sample data.
+
+![Manual strategy compared to RT learner.](figure_2.png)
+
+I have also implemented a strategy learner based on Q-learning. The
+Q-learner uses fifteen training runs on the in-sample data. It mostly
+does well for the out of sample data, but it looks like the RT-based
+strategy learner is better.
+
+I am using a bin-size of five for the three indicators mentioned before.
+That results in 375 (3x5x5x5) states with only about 500 in-sample data
+points. Probably the Q-learner is overfitting to the in-sample data.
+Indeed, with bin sizes of four, the Q learner performs better for the
+out-of-sample data.
+
+![Strategy learner based on Q-Learning with using four and five bins
+for discretization out of sample.](figure_4.png)
+
+# Experiment 2
+
+Experiment 2 aims to show that the strategy learner trades differently
+when there is a commission, and the impact is not zero. The RT-based
+trader does not consider the commission value, but the Q-learning based
+trader does.
+
+However, it seems like a commission smaller than $10 does not affect
+the number of trades significantly. Only when the commission is around
+$50 or with a slippage of 1% we see considerably fewer transactions.
+
+| commission | n_orders |
+|------------|----------|
+| 9.95       | 79       |
+| 20         | 83       |
+| 50         | 63       |
+| 100        | 37       |
+
+# Closing Remarks
+
+Machine Learning for Trading is a great course. It gives an excellent
+introduction to finance, trading, and machine learning without getting lost in
+technical or mathematical details. I have enjoyed building decision tree
+learners and a Q learner from first principles. At the same time, the course
+accurately teaches powerful libraries such as NumPy and Pandas.
+

strategy_evaluation/testproject.py

@@ -0,0 +1,8 @@
+from experiment1 import experiment1
+from experiment2 import experiment2
+
+
+if __name__ == "__main__":
+    experiment1(create_report=True)
+    experiment2()
+

util.py

@@ -0,0 +1,68 @@
+"""MLT: Utility code.
+
+Copyright 2017, Georgia Tech Research Corporation
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+"""
+
+import os
+import pandas as pd
+
+def symbol_to_path(symbol, base_dir=None):
+    """Return CSV file path given ticker symbol."""
+    if base_dir is None:
+        base_dir = os.environ.get("MARKET_DATA_DIR", '../data/')
+    return os.path.join(base_dir, "{}.csv".format(str(symbol)))
+
+def get_data(symbols, dates, addSPY=True, colname='Adj Close', datecol='Date'):
+    """Read stock data (adjusted close) for given symbols from CSV files."""
+    df = pd.DataFrame(index=dates)
+    if addSPY and 'SPY' not in symbols:  # add SPY for reference, if absent
+        # handles the case where symbols is np array of 'object'
+        symbols = ['SPY'] + list(symbols)
+
+    for symbol in symbols:
+        if 'BTC' in symbol or 'ETH' in symbol:
+            colname = 'close'
+            datecol = 'time'
+        elif symbol == 'SPY':
+            colname = 'close'
+            datecol = 'time'
+        else:
+            colname = 'Adj Close'
+            datecol = 'Date'
+
+        df_temp = pd.read_csv(symbol_to_path(symbol),
+                              index_col=datecol,
+                              parse_dates=True, usecols=[datecol, colname],
+                              na_values=['nan'])
+        df_temp = df_temp.rename(columns={colname: symbol})
+
+        if datecol == 'time':
+            df_temp['date'] = pd.to_datetime(df_temp.index, unit='s')
+            df_temp['date'] = pd.DatetimeIndex(df_temp['date']).normalize()
+            df_temp.set_index('date', drop=True, inplace=True)
+
+        df = df.join(df_temp)
+        if symbol == 'SPY':  # drop dates SPY did not trade
+            pass
+            # df = df.dropna(subset=["SPY"])
+    return df
+
+
+def plot_data(df, title="Stock prices", xlabel="Date", ylabel="Price"):
+    import matplotlib.pyplot as plt
+    """Plot stock prices with a custom title and meaningful axis labels."""
+    ax = df.plot(title=title, fontsize=12)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    plt.show()
+
+def get_orders_data_file(basefilename):
+    return open(os.path.join(os.environ.get("ORDERS_DATA_DIR",'orders/'),basefilename))
+
+def get_learner_data_file(basefilename):
+    return open(os.path.join(os.environ.get("LEARNER_DATA_DIR",'Data/'),basefilename),'r')
+
+def get_robot_world_file(basefilename):
+    return open(os.path.join(os.environ.get("ROBOT_WORLDS_DIR",'testworlds/'),basefilename))