def computeReturnRate(priceVec, stockType): capital=1000 # Initial available capital capitalOrig=capital # original capital dataCount=len(priceVec) # day size suggestedAction=np.zeros((dataCount,1)) # Vec of suggested actions stockHolding=np.zeros((dataCount,1)) # Vec of stock holdings total=np.zeros((dataCount,1)) # Vec of total asset realAction=np.zeros((dataCount,1)) # Real action, which might be different from suggested action. For instance, when the suggested action is 1 (buy) but you don't have any capital, then the real action is 0 (hold, or do nothing). # Run through each day for ic in range(dataCount): currentPrice=priceVec[ic] # current price suggestedAction[ic]=myStrategy(priceVec[0:ic], currentPrice, stockType) # Obtain the suggested action # get real action by suggested action if ic>0: stockHolding[ic]=stockHolding[ic-1] # The stock holding from the previous day if suggestedAction[ic]==1: # Suggested action is "buy" if stockHolding[ic]==0: # "buy" only if you don't have stock holding stockHolding[ic]=capital/currentPrice # Buy stock using cash capital=0 # Cash realAction[ic]=1 elif suggestedAction[ic]==-1: # Suggested action is "sell" if stockHolding[ic]>0: # "sell" only if you have stock holding capital=stockHolding[ic]*currentPrice # Sell stock to have cash stockHolding[ic]=0 # Stocking holding realAction[ic]=-1 elif suggestedAction[ic]==0: # No action realAction[ic]=0 else: assert False total[ic]=capital+stockHolding[ic]*currentPrice # Total asset, including stock holding and cash returnRate=(total[-1]-capitalOrig)/capitalOrig # Return rate of this run return returnRate
def calculateReturnRate(file, stocksType):
# stocksType = "SPY" or "IAU" or "DSI" , "LQD"
# read file
df = pd.read_csv(file)
adjClose = df["Adj Close"].values # get adj close
dataCount = len(adjClose) # day size
# init.
capital = 1 # 持有資金
capitalOrig = capital # cost
suggestedAction = np.zeros((dataCount, 1)) # 判斷action
stockHolding = np.zeros((dataCount, 1)) # 持有股票
total = np.zeros((dataCount, 1)) # 結算資金
realAction = np.zeros((dataCount, 1)) # 實際action
# run each day
for ic in range(dataCount):
currPrice = adjClose[ic] # 當天價格
suggestedAction[ic] = myStrategy(adjClose[0:ic], currPrice,
stocksType) # 取得當天action
# get real action by suggested action
if ic > 0:
# 更新手上持有股票
stockHolding[ic] = stockHolding[ic - 1]
if suggestedAction[ic] == 1:
# 若未持有股票: 買
if stockHolding[ic] == 0:
stockHolding[ic] = capital / currPrice # 買入股票
capital = 0 # 持有資金
realAction[ic] = 1
elif suggestedAction[ic] == -1:
# 若持有股票： 賣
if stockHolding[ic] > 0:
capital = stockHolding[ic] * currPrice # 賣出股票
stockHolding[ic] = 0 # 持有股票
realAction[ic] = -1
elif suggestedAction[ic] == 0:
# 不買不賣
realAction[ic] = 0
else:
assert False
# 當天結算資金
total[ic] = capital + stockHolding[ic] * currPrice
# 最終盈利率
returnRate = (total[-1] - capitalOrig) / capitalOrig
return returnRate
evalDays = 14
action = np.zeros((evalDays,1))
realAction = np.zeros((evalDays,1))
total = np.zeros((evalDays,1))
total[0] = capital
Holding = 0.0
openPricev = dailyOhlcv["open"].tail(evalDays).values
clearPrice = dailyOhlcv.iloc[-3]["close"]
for ic in range(evalDays,0,-1):
flag = 1
dailyOhlcvFile = dailyOhlcv.head(len(dailyOhlcv)-ic)
dateStr = dailyOhlcvFile.iloc[-1,0]
minutelyOhlcvFile = minutelyOhlcv.head((np.where(minutelyOhlcv.iloc[:,0].str.split(expand=True)[0].values==dateStr))[0].max()+1)
action[evalDays-ic] = myStrategy(dailyOhlcvFile,minutelyOhlcvFile,openPricev[evalDays-ic])
currPrice = openPricev[evalDays-ic]
if action[evalDays-ic] == 1:
if Holding == 0 and capital > transFee:
Holding = (capital-transFee)/currPrice
capital = 0
realAction[evalDays-ic] = 1
elif action[evalDays-ic] == -1:
if Holding > 0 and Holding*currPrice > transFee:
capital = Holding*currPrice - transFee
Holding = 0
realAction[evalDays-ic] = -1
elif action[evalDays-ic] == 0:
realAction[evalDays-ic] = 0
else:
assert False
import pandas as pd
from myStrategy import myStrategy
df = pd.read_csv(sys.argv[1])
adjClose = df["Adj Close"].values
capital = 1
capitalOrig = capital
dataCount = len(adjClose)
suggestedAction = np.zeros((dataCount, 1))
stockHolding = np.zeros((dataCount, 1))
total = np.zeros((dataCount, 1))
realAction = np.zeros((dataCount, 1))
total[0] = capital
for ic in range(dataCount):
currPrice = adjClose[ic]
suggestedAction[ic] = myStrategy(adjClose[0:ic], currPrice)
if ic > 0:
stockHolding[ic] = stockHolding[ic - 1]
if suggestedAction[ic] == 1:
if stockHolding[ic] == 0:
stockHolding[ic] = capital / currPrice
capital = 0
realAction[ic] = 1
elif suggestedAction[ic] == -1:
if stockHolding[ic] > 0:
capital = stockHolding[ic] * currPrice
stockHolding[ic] = 0
realAction[ic] = -1
elif suggestedAction[ic] == 0:
realAction[ic] = 0
else: