Finish manual strategy for project 8

I struggled with the manual strategy, mostly because I tried to read
good triggers from the price action charts. Finally, I had the ingenious
(hmm) idea to scatter plot the 1, 3, and 5 day percentage returns over
different indicators. I can also use this information to train my Q
learner.

strategy_evaluation/ManualStrategy.py

@@ -58,19 +58,32 @@ class ManualStrategy:
         orders['Shares'] = orders['Shares'].rolling(2).apply(strat)
 
     def three_indicator_strat(self, macd, rsi, price_sma, orders):
-        # XXX: continue here
-
         def strat(row):
-            m = macd.loc[row.index]
+            shares = 0
-            print(m)
+            _, _, macd_diff = macd.loc[row.name]
-            # mac, signal = m.iloc[0]
+            cur_rsi = rsi.loc[row.name][0]
-            # print(mac, signal)
+            cur_price_sma = price_sma.loc[row.name][0]
-            return 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)
-        raise Exception()
 
-    # 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),
@@ -87,10 +100,9 @@ class ManualStrategy:
 
         macd = indicators.macd(df, symbol)
         rsi = indicators.rsi(df, symbol)
-        price_sma = indicators.price_sma(df, symbol, [21])
+        price_sma = indicators.price_sma(df, symbol, [8])
+
         # self.macd_strat(macd, orders)
         self.three_indicator_strat(macd, rsi, price_sma, orders)
-
-        # heers = orders[orders["Shares"] != 0]
         return orders
 

strategy_evaluation/experiment1.py

@@ -1,22 +1,76 @@
 import pandas as pd
 import datetime as dt
-import marketsim.marketsim as marketsim
+import sys
-import indicators
+
 import util
+import indicators
+import marketsim.marketsim as marketsim
 import matplotlib.pyplot as plt
 from matplotlib.widgets import MultiCursor
 from BenchmarkStrategy import BenchmarkStrategy
 from ManualStrategy import ManualStrategy
 
 
+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)
+    sma = indicators.sma(df, symbol, [8])
+    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()
+    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 experiment1():
     symbol = "JPM"
     start_value = 10000
-    sd = dt.datetime(2008, 1, 1)
+    sd = dt.datetime(2008, 1, 1)  # in-sample
-    ed = dt.datetime(2009, 12, 31)
+    ed = dt.datetime(2009, 12, 31)  # in-sample
+    # sd = dt.datetime(2010, 1, 1)  # out-sample
+    # ed = dt.datetime(2011, 12, 31)  # out-sample
+
     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)
@@ -26,21 +80,12 @@ def experiment1():
     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()
 
-    price_sma = indicators.price_sma(df, symbol, [21])
+    fig, ax = plt.subplots(3, sharex=True)
-    bb = indicators.bollinger_band(df, symbol)
-    sma = indicators.sma(df, symbol, [9, 21])
-    rsi = indicators.rsi(df, symbol)
-    macd = indicators.macd(df, symbol).copy()
-
-    fig, ax = plt.subplots(4, sharex=True)
     df[[symbol]].plot(ax=ax[0])
-    # bb.plot(ax=ax[0])
+    df[["Benchmark", "Manual"]].plot(ax=ax[1])
-    price_sma.plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
-    macd.plot(ax=ax[2])
-    rsi.plot(ax=ax[3])
-    # df[["Benchmark", "Manual"]].plot(ax=ax[1])
-    # df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
 
     for a in ax:
         a.grid()
@@ -48,11 +93,7 @@ def experiment1():
     plt.show()
     # plt.savefig('figure_1.png')
 
-    # You may use data from other symbols (such as SPY) to inform both your
-    # Manual Learner and Strategy Learner. The in-sample/development period is
-    # January 1, 2008 to December 31 2009. The out-of-sample/testing period is
-    # January 1, 2010 to December 31 2011.
-
 
 if __name__ == "__main__":
     experiment1()
+

strategy_evaluation/indicators.py

@@ -17,9 +17,7 @@ def bollinger_band(df, symbol, period=20, m=2):
     std = df[symbol].rolling(period).std()
     boll_up = boll_sma + m * std
     boll_lo = boll_sma - m * std
-    key_sma = f"{symbol}-Boll({period})-sma"
+    key_sma, key_up, key_lo = "boll_sma", "boll_up", "boll_lo"
-    key_up = f"{symbol}-Boll({period})-up"
-    key_lo = f"{symbol}-Boll({period})-lo"
     df[key_sma] = boll_sma
     df[key_up] = boll_up
     df[key_lo] = boll_lo
@@ -32,7 +30,7 @@ def sma(df, symbol, period):
         period = [period]
     keys = []
     for p in period:
-        key = f"{symbol}-sma({p})"
+        key = f"sma_{p}"
         df[key] = df[symbol].rolling(p).mean()
         keys.append(key)
     return df[keys]
@@ -44,8 +42,8 @@ def ema(df, symbol, period):
         period = [period]
     keys = []
     for p in period:
-        key = f"{symbol}-ema({p})"
+        key = f"ema_{p}"
-        df[key] = df[symbol].ewm(span=period).mean()
+        df[key] = df[symbol].ewm(span=p).mean()
         keys.append(key)
     return df[keys]
 
@@ -56,7 +54,7 @@ def price_sma(df, symbol, period):
         period = [period]
     keys = []
     for p in period:
-        key = f"{symbol}-price/sma({p})"
+        key = f"price_sma_{p}"
         sma = df[symbol].rolling(p).mean()
         df[key] = df[symbol] / sma
         keys.append(key)
@@ -84,16 +82,28 @@ def rsi(df, symbol, period=14):
 
 def macd(df, symbol):
     macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
-    k1 = f"macd"
+    k1 = "macd"
-    k2 = k1 + "-signal"
+    k2 = "macd_signal"
+    k3 = "macd_diff"
     df[k1] = macd
     df[k2] = macd.rolling(9).mean()
-    return df[[k1, k2]]
+    df[k3] = df[k1] - df[k2]
+    return df[[k1, k2, k3]]
 
 
 def price_delta(df, symbol, period=1):
     """Calculate delta between previous day and today."""
-    df[f"{symbol}-diff({period})"] = df[symbol].diff(periods=period)
+    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}"
+    df[k] = df[symbol].pct_change(periods=period)
+    df[k] = df[k].shift(-period)
+    return df[k]
 
 
 def test_indicators():
@@ -135,4 +145,3 @@ def test_indicators():
     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')
-