28 lines
1.3 KiB
Python
28 lines
1.3 KiB
Python
# Trading Log Price Spread
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import numpy as np
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import pandas as pd
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import statsmodels.formula.api as sm
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import statsmodels.tsa.stattools as ts
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df=pd.read_csv('inputData_GLD_USO.csv')
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df['Date']=pd.to_datetime(df['Date'], format='%Y%m%d').dt.date # remove HH:MM:SS
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df.set_index('Date', inplace=True)
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lookback=20
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hedgeRatio=np.full(df.shape[0], np.nan)
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for t in np.arange(lookback, len(hedgeRatio)):
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regress_results=sm.ols(formula="USO ~ GLD", data=np.log(df[(t-lookback):t])).fit() # Note this can deal with NaN in top row
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hedgeRatio[t-1]=regress_results.params[1]
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yport=np.sum(ts.add_constant(-hedgeRatio)[:, [1,0]]*np.log(df), axis=1)
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yport.plot()
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# Apply a simple linear mean reversion strategy to GLD-USO
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numUnits =-(yport-yport.rolling(lookback).mean())/yport.rolling(lookback).std() # capital invested in portfolio in dollars. movingAvg and movingStd are functions from epchan.com/book2
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positions=pd.DataFrame(np.tile(numUnits.values, [2, 1]).T * ts.add_constant(-hedgeRatio)[:, [1,0]] ) # positions is the dollar capital in each ETF.
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pnl=np.sum((positions.shift().values)*(df.pct_change().values), axis=1) # daily P&L of the strategy
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ret=pnl/np.sum(np.abs(positions.shift()), axis=1)
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(np.cumprod(1+ret)-1).plot()
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print('APR=%f Sharpe=%f' % (np.prod(1+ret)**(252/len(ret))-1, np.sqrt(252)*np.mean(ret)/np.std(ret)))
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