100 lines
2.9 KiB
Python
100 lines
2.9 KiB
Python
import pandas as pd
|
|
import datetime as dt
|
|
import sys
|
|
|
|
import util
|
|
import indicators
|
|
import marketsim.marketsim as marketsim
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.widgets import MultiCursor
|
|
from BenchmarkStrategy import BenchmarkStrategy
|
|
from ManualStrategy import ManualStrategy
|
|
|
|
|
|
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) # 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)
|
|
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()
|
|
|
|
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])
|
|
|
|
for a in ax:
|
|
a.grid()
|
|
multi = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
|
|
plt.show()
|
|
# plt.savefig('figure_1.png')
|
|
|
|
|
|
if __name__ == "__main__":
|
|
experiment1()
|
|
|