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Finish manual strategy for project 8

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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.
This commit is contained in:
Felix Martin
2020-11-03 19:05:43 -05:00
parent 43e297c075
commit 0519ae9336
3 changed files with 108 additions and 46 deletions
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85
strategy_evaluation/experiment1.py
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@@ -1,22 +1,76 @@
import pandas as pd
import datetime as dt
import marketsim.marketsim as marketsim
import indicators
import sys
import util
import indicators
import marketsim.marketsim as marketsim
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor
from BenchmarkStrategy import BenchmarkStrategy
from ManualStrategy import ManualStrategy
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)
ed = dt.datetime(2009, 12, 31)
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
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])
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)
fig, ax = plt.subplots(3, sharex=True)
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])
# df[["Benchmark", "Manual"]].plot(ax=ax[1])
# df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
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()