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Implement SMA, price/SMA, and bollinger band

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This commit is contained in:
Felix Martin
2020-10-13 20:46:56 -04:00
parent 3e12fda047
commit 5ac3d46c1f
1 changed files with 37 additions and 14 deletions
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51
manual_strategy/indicators.py
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@@ -11,7 +11,7 @@ def author():
return "felixm" return "felixm"
def test_policy(): def test_optimal_strategy():
symbol = "JPM" symbol = "JPM"
start_value = 100000 start_value = 100000
sd = dt.datetime(2008, 1, 1) sd = dt.datetime(2008, 1, 1)
@@ -55,7 +55,7 @@ def test_policy():
portvals["Bench"] = bench["Portval"] portvals["Bench"] = bench["Portval"]
portvals.drop(columns=["Portval"], inplace=True) portvals.drop(columns=["Portval"], inplace=True)
ax = portvals.plot(title="Optimal strategy versus 1000 shares of JPM") portvals.plot(title="Optimal strategy versus 1000 shares of JPM")
plt.savefig('figure_5.png') plt.savefig('figure_5.png')
@@ -63,23 +63,46 @@ def normalize(timeseries):
return timeseries / timeseries.iloc[0] return timeseries / timeseries.iloc[0]
def bollinger_band(prices): def bollinger_band(df, symbol, period=20, m=2):
pass 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
df[f"Boll({symbol}, {period})-sma"] = boll_sma
df[f"Boll({symbol}, {period})-up"] = boll_up
df[f"Boll({symbol}, {period})-lo"] = boll_lo
def sma(df, symbol, period):
"""Adds a new column to the dataframe SMA(period)"""
df[f"{symbol}-sma({period})"] = df[symbol].rolling(period).mean()
def price_sma(df, symbol, period):
"""Calculates SMA and adds new column price divided by SMA to the df."""
sma = df[symbol].rolling(period).mean()
df[f"{symbol}-price/sma({period})"] = df[symbol] / sma
def main(): def main():
test_policy() # test_optimal_strategy()
symbol = "JPM"
# plot_data(prices) sd = dt.datetime(2008, 1, 1)
# sd = dt.datetime(2008, 1, 1) ed = dt.datetime(2009, 12, 31)
# ed = dt.datetime(2009, 12, 31) df = get_data([symbol], pd.date_range(sd, ed))
# prices = get_data(['JPM'], pd.date_range(sd, ed)) df.drop(columns=["SPY"], inplace=True)
# prices['JPM'] = normalize(prices['JPM']) df = normalize(df)
# print(prices)
# plot_data(prices) sma(df, symbol, 8)
# prices_appl = get_data(['AAPL'], pd.date_range(sd, ed), 'High') # price_sma(df, symbol, 8)
# prices['AAPL'] = prices_appl['AAPL'] # bollinger_band(df, symbol)
# TODO
# rsi(df, symbol)
# macd(df, symbol)
plot_data(df)
if __name__ == "__main__": if __name__ == "__main__":