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

@@ -39,5 +39,5 @@ unzip -n zips/*.zip -d ./
 - No reports for projects 4 (defeat learners) and 5 (marketsim)
 - [Report 6](./manual_strategy/manual_strategy.md)
 - 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()
+

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/ManualStrategy.py

@@ -36,18 +36,21 @@ class ManualStrategy:
             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]
+            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 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:
+            elif cur_macd > 1 and prev_macd > prev_signal \
+                              and cur_macd < cur_signal:
                 if self.holding == 0:
                     shares = -1000
                 elif self.holding == 1000:
@@ -58,6 +61,8 @@ class ManualStrategy:
         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]
@@ -87,7 +92,7 @@ class ManualStrategy:
     def testPolicy(self, symbol="IBM",
                    sd=dt.datetime(2009, 1, 1),
                    ed=dt.datetime(2010, 1, 1),
-                   sv=10000):
+                   sv=10000, macd_strat=False):
 
         self.holding = 0
         df = util.get_data([symbol], pd.date_range(sd, ed))
@@ -102,7 +107,8 @@ class ManualStrategy:
         rsi = indicators.rsi(df, symbol)
         price_sma = indicators.price_sma(df, symbol, [8])
 
-        # self.macd_strat(macd, orders)
-        self.three_indicator_strat(macd, rsi, price_sma, orders)
+        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

@@ -1,88 +1,106 @@
-"""
-Template for implementing StrategyLearner  (c) 2016 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---
-
-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 datetime as dt
 import pandas as pd
-import util as ut
+import util
+import indicators
+from RTLearner import RTLearner
+
 
 class StrategyLearner(object):
 
-    # constructor
-    def __init__(self, verbose = False, impact=0.0, commission=0.0):
+    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
 
-    # 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 = ut.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
-        volume_all = ut.get_data(syms, dates, colname = "Volume")  # automatically adds SPY
+    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)
+        if self.verbose:
+            print(volume)
 
-    # this method should use the existing policy and test it against new data
-    def testPolicy(self, symbol = "IBM", \
-        sd=dt.datetime(2009,1,1), \
-        ed=dt.datetime(2010,1,1), \
-        sv = 10000):
+    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)
 
-        # here we build a fake set of trades
-        # your code should return the same sort of data
-        dates = pd.date_range(sd, ed)
-        prices_all = ut.get_data([symbol], dates)  # automatically adds SPY
-        trades = prices_all[[symbol,]]  # only portfolio symbols
-        # trades_SPY = prices_all['SPY']  # only SPY, for comparison later
-        trades.values[:,:] = 0 # set them all to nothing
-        trades.values[0,:] = 1000 # add a BUY at the start
-        trades.values[40,:] = -1000 # add a SELL
-        trades.values[41,:] = 1000 # add a BUY
-        trades.values[60,:] = -2000 # go short from long
-        trades.values[61,:] = 2000 # go long from short
-        trades.values[-1,:] = -1000 #exit on the last day
-        if self.verbose: print(type(trades)) # it better be a DataFrame!
-        if self.verbose: print(trades)
-        if self.verbose: print(prices_all)
-        return trades
+    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"]]
 
-if __name__=="__main__":
-    print("One does not simply think up a strategy")

strategy_evaluation/experiment1.py

@@ -9,6 +9,8 @@ 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):
@@ -16,7 +18,6 @@ def plot_indicators(symbol, df):
 
     price_sma = indicators.price_sma(df, symbol, [8])
     bb = indicators.bollinger_band(df, symbol)
-    sma = indicators.sma(df, symbol, [8])
     rsi = indicators.rsi(df, symbol)
     macd = indicators.macd(df, symbol).copy()
 
@@ -27,6 +28,7 @@ def plot_indicators(symbol, df):
     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)
 
@@ -57,43 +59,178 @@ def visualize_correlations(symbol, df):
     sys.exit(0)
 
 
-def experiment1():
-    symbol = "JPM"
-    start_value = 10000
-    sd = dt.datetime(2008, 1, 1)  # in-sample
-    ed = dt.datetime(2009, 12, 31)  # in-sample
-    # sd = dt.datetime(2010, 1, 1)  # out-sample
-    # ed = dt.datetime(2011, 12, 31)  # out-sample
+def compare_manual_strategies(symbol, sv, sd, ed):
 
     df = util.get_data([symbol], pd.date_range(sd, ed))
     df.drop(columns=["SPY"], inplace=True)
 
-    # visualize_correlations(symbol, df)
-    # plot_indicators(symbol, df)
-
     bs = BenchmarkStrategy()
-    orders = bs.testPolicy(symbol, sd, ed, start_value)
-    df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
+    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, start_value)
-    df["Manual"] = marketsim.compute_portvals(orders, start_value)
-    df["Orders Manual"] = orders["Shares"]
-    df["Holding Manual"] = orders["Shares"].cumsum()
+    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", "Manual"]].plot(ax=ax[1])
-    df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
+    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()
-    multi = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    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()
-    # plt.savefig('figure_1.png')
+    # 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

@@ -1,242 +1,242 @@
-"""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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+"""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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     ((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" \
@@ -244,96 +244,96 @@ def test_strategy(description, insample_args, outsample_args, benchmark_type, be
                          "    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__])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                         "    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

@@ -73,7 +73,7 @@ def rsi(df, symbol, period=14):
                            (avg_loss / period))))
         return rsi
 
-    key = f"rsi"
+    key = "rsi"
     # Add one to get 'period' price changes (first change is nan).
     period += 1
     df[key] = df[symbol].rolling(period).apply(rsi)
@@ -91,13 +91,6 @@ def macd(df, symbol):
     return df[[k1, k2, k3]]
 
 
-def price_delta(df, symbol, period=1):
-    """Calculate delta between previous day and today."""
-    k = f"diff_{period}"
-    df[k] = df[symbol].diff(periods=period)
-    return df[k]
-
-
 def price_delta(df, symbol, period=1):
     """Calculate percentage change for period."""
     k = f"pct_{period}"

strategy_evaluation/strategy_evaluation.md

@@ -1,3 +1,75 @@
-# Report
+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.
 
-![First strategy based on MACD. Better than just holding](figure_1.png)

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))