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Implement binning and state calculation

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Felix Martin
2020-11-07 12:39:37 -05:00
parent 889bcf68ca
commit 169dd8278d
2 changed files with 89 additions and 54 deletions
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17
strategy_evaluation/experiment1.py
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@@ -147,13 +147,15 @@ def experiment1(create_report=False):
# visualize_correlations(symbol, df)
# plot_indicators(symbol, df)
bs = BenchmarkStrategy()
orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
df["Benchmark"] = marketsim.compute_portvals(orders, sv)
df["Orders Benchmark"] = orders["Shares"]
# bs = BenchmarkStrategy()
# orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
# df["Benchmark"] = marketsim.compute_portvals(orders, sv)
# df["Orders Benchmark"] = orders["Shares"]
ql = QLearner(testing=True)
ql = QLearner(testing=True, verbose=True)
ql.addEvidence(symbol, sd, ed, sv)
return
orders = ql.testPolicy(symbol, sd_out, ed_out, sv)
df["QL"] = marketsim.compute_portvals(orders, sv)
df["Orders QL"] = orders["Shares"]
@@ -168,11 +170,6 @@ def experiment1(create_report=False):
m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
plt.show()
# For debugging the classification learner:
# df["y_train"] = sl.addEvidence(symbol, sd, ed, sv)
# df["y_query"] = sl.testPolicy(symbol, sd, ed, sv)
# df[["y_train", "y_query"]].plot(ax=ax[1])
if __name__ == "__main__":
experiment1()