import sys import numpy as np import pandas as pd from myOptimAction import myOptimAction from profitEstimateOpen import profitEstimateOpen df = pd.read_csv(sys.argv[1]) transFeeRate= float(sys.argv[2]) priceVec = df["Adj Close"].values actionVec = myOptimAction(priceVec, transFeeRate) returnRate = profitEstimateOpen(priceVec, transFeeRate, actionVec)
stockHolding[i][a] -= sellStock # Stocking holding
stockHolding[i][b] += getCash * (
1 - transFeeRate) / currentPriceBuy # Buy stock using cash
realAction[i] = 2
else:
assert False
else:
assert False
# calculate total cash you get at last day
total = capital
for stock in range(stockCount):
currentPriceVec = priceMat[actionMat[-1][0]]
total += stockHolding[-1][stock] * currentPriceVec[stock] * (
1 - transFeeRate) # Total asset, including stock holding and cash
returnRate = (total - capitalOrig) / capitalOrig # Return rate of this run
return returnRate
if __name__ == "__main__":
print("Reading %s..." % (sys.argv[1]))
file = sys.argv[1] # input file
df = pd.read_csv(file, delimiter=' ')
transFeeRate = float(sys.argv[2]) # Rate for transaction fee
priceMat = df.values # Get price as the m×n matrix which holds n stocks' price over m days
actionMat = myOptimAction(priceMat,
transFeeRate) # Obtain the suggested action
rr = computeReturnRate(priceMat, transFeeRate,
actionMat) # Compute return rate
print("rr=%f%%" % (rr * 100))
#import sys
#import numpy as np
import pandas as pd
from myOptimAction import myOptimAction
from profitEstimateOpen import profitEstimateOpen
#df = pd.read_csv(sys.argv[1])
#transFeeRate= float(sys.argv[2])
df = pd.read_csv("SPY.csv")
transFeeRate= float(0.01)
priceVec = df["Adj Close"].values
actionVec = myOptimAction(priceVec, transFeeRate)
returnRate = profitEstimateOpen(priceVec, transFeeRate, actionVec)
print(returnRate[0])