def runBacktest(self, path, longWindow, shortWindow, display):
dataframe = pd.read_csv(path)
count = len(dataframe)
for i in range(1, count):
heuristicResult = self.heuristic(dataframe, longWindow, shortWindow, i)
lastPrice = dataframe.iloc[i]["close"]
date = dataframe.iloc[i]["timestamp"]
if heuristicResult == 1 and not self.isLong():
""" Executing ORDINARY BUY order """
self.balance, self.unitsAvailable, self.stopLoss, self.takeProfit = tradeUtil.ordinaryBuyMax(dataframe.iloc[i]["timestamp"], self.balance, dataframe.iloc[i]["close"], self.unitsAvailable, display)
self.lastBuyIndex = i
self.buyDates.append(pd.to_datetime(date))
elif self.isLong() and (lastPrice < self.stopLoss or lastPrice > self.takeProfit):
if lastPrice < self.stopLoss:
""" Executing STOP LOSS SELL order """
self.balance, self.unitsAvailable = tradeUtil.stopLossSell(dataframe.iloc[i]["timestamp"], self.balance, self.stopLoss, self.unitsAvailable, display)
diff1 = dataframe.iloc[i-1]["close"] - dataframe.iloc[i]["close"]
diff2 = dataframe.iloc[i-1]["close"] - self.stopLoss
frac = (diff2/diff1)*24
self.sellDates.append(pd.to_datetime(date)-timedelta(hours=24-frac))
elif lastPrice > self.takeProfit:
""" Executing TAKE PROFIT SELL order """
self.balance, self.unitsAvailable = tradeUtil.takeProfitSell(dataframe.iloc[i]["timestamp"], self.balance, dataframe.iloc[i]["close"], self.unitsAvailable, display)
self.sellDates.append(pd.to_datetime(date))
""" Transferring excess funds from balance to savings """
if self.balance > self.startingBalance:
self.saved += self.balance - self.startingBalance
self.balance = self.startingBalance
""" If last BUY order has no corresponding SELL order, do not include it in profit/loss calculations. """
if self.isLong():
if display:
print("NOTE: Last BUY entry printed above was not closed, and is not factored in Profit/Loss calculation.")
self.buyDates = self.buyDates[:-1]
self.balance += dataframe.iloc[self.lastBuyIndex]["close"] * self.unitsAvailable
self.balance = self.balance + self.saved
returnPercentage = 100*((self.balance-self.startingBalance)/self.startingBalance)
period = util.periods(path)/365
avgReturnPercentage = round(returnPercentage/period, 2)
period = round(period, 2)
returnPercentage = round(returnPercentage, 2)
self.balance = round(self.balance, 2)
print("\nReturn: " + str(returnPercentage) + "% over a period of " + str(period) + " years (" + str(avgReturnPercentage) + "% per annum)")
sys.stdout.flush()
return self.buyDates, self.sellDates
def smaCrossover(path, window):
smaColumn = "sma" + str(window)
dataframe = pd.read_csv(path)
count = len(dataframe)
startingBalance = 1000000
balance = startingBalance
unitsAvailable = 0
long = False
for i in range(0, count):
if (dataframe.iloc[i]["close"] >
dataframe.iloc[i][smaColumn]) and (not long):
qty = math.floor(balance / dataframe.iloc[i]["close"])
print(
str(dataframe.iloc[i]["timestamp"]) + ": BUY " + str(qty) +
" units at $" + str(dataframe.iloc[i]["close"]))
long = True
balance -= dataframe.iloc[i]["close"] * qty
unitsAvailable += qty
elif (dataframe.iloc[i]["close"] <
dataframe.iloc[i][smaColumn]) and (long):
print(
str(dataframe.iloc[i]["timestamp"]) + ": SELL " +
str(unitsAvailable) + " at $" +
str(dataframe.iloc[i]["close"]))
long = False
balance += dataframe.iloc[i]["close"] * unitsAvailable
unitsAvailable = 0
if unitsAvailable > 0:
balance += dataframe.iloc[0]["close"] * unitsAvailable
returnPercentage = 100 * ((balance - startingBalance) / startingBalance)
period = util.periods(dataframe) / 365
avgReturnPercentage = round(returnPercentage / period, 2)
period = round(period, 2)
returnPercentage = round(returnPercentage, 2)
balance = round(balance, 2)
print("\nBalance: " + str(balance) + "\nReturn Percentage: " +
str(returnPercentage) + "% over a period of " + str(period) +
" years.")
print("Average Yearly Return Percentage: " + str(avgReturnPercentage) +
"%")
print("Buy and Hold Return Percentage would have been: " + str(
round(
100 *
(dataframe.iloc[count - 1]['close'] - dataframe.iloc[0]['close']) /
dataframe.iloc[0]['close'], 2)) + "%")
print("I.e. Bought at $" + str(dataframe.iloc[0]['close']) + " on " +
str(dataframe.iloc[0]['timestamp'] + " and sold at $" +
str(dataframe.iloc[count - 1]['close']) + " on " +
str(dataframe.iloc[count - 1]['timestamp'])))
print("--------------------")
sys.stdout.flush()
def runBacktest(self, path, window):
dataframe = pd.read_csv(path)
count = len(dataframe)
for i in range(0, count):
heuristicResult = self.heuristic(dataframe, window, i)
if heuristicResult == 1 and not self.isLong():
self.balance, self.unitsAvailable, self.stopLoss = tradeUtil.ordinaryBuyMax(
dataframe.iloc[i]["timestamp"], self.balance,
dataframe.iloc[i]["close"], self.unitsAvailable)
self.lastBuyIndex = i
elif heuristicResult == 0 and self.isLong():
self.balance, self.unitsAvailable = tradeUtil.ordinarySellMax(
dataframe.iloc[i]["timestamp"], self.balance,
dataframe.iloc[i]["close"], self.unitsAvailable)
# Make sure last buy was closed --- fix this
if self.isLong():
print(
"NOTE: Last BUY entry printed above is invalid. Please ignore."
)
self.balance += dataframe.iloc[
self.lastBuyIndex]["close"] * self.unitsAvailable
returnPercentage = 100 * (
(self.balance - self.startingBalance) / self.startingBalance)
period = util.periods(dataframe) / 365
avgReturnPercentage = round(returnPercentage / period, 2)
period = round(period, 2)
returnPercentage = round(returnPercentage, 2)
self.balance = round(self.balance, 2)
print("\nReturn: " + str(returnPercentage) + "% over a period of " +
str(period) + " years (" + str(avgReturnPercentage) +
"% per annum)")
print("--------------------")
sys.stdout.flush()
if forcePull and os.path.isfile(rawPath):
os.unlink(rawPath)
if os.path.isfile(enrichedPath):
os.unlink(enrichedPath)
""" Pull and/or Enrich pricing data """
pullAndEnrich.pullAndEnrich(ticker, "full", rawPath, enrichedPath, indicators)
""" Backtest Random strategy """
util.printPartition()
print("Simulating Random Strategy " + str(randomStrategyCount) + " times.")
randomReturn = randomStrategy.runMultipleRandoms(enrichedPath, randomStrategyCount)
print("\nRandom strategy would have returned: " + str(randomReturn) + "% over a period of " + str(round(util.periods(enrichedPath)/365, 2)) + " years (on average).")
sys.stdout.flush()
""" Backtest each strategy requested """
for strategy in strategies:
if "smac" in strategy:
horizon = strategy[4:]
util.printPartition()
print("Simulating SMA Crossover Strategy with time horizon: " + horizon)
sys.stdout.flush()
buys, sells = SMACrossover.SMACrossover().runBacktest(enrichedPath, int(horizon), display)
if plotBacktests:
simplePlt.plot(ticker, enrichedPath, buys, sells, "SMA Crossover (" + horizon + " day) Strategy Backtest")
def smaLongShortCrossover(path, longWindow, shortWindow):
longSMAColumn = "sma" + str(longWindow)
shortSMAColumn = "sma" + str(shortWindow)
dataframe = pd.read_csv(path)
count = len(dataframe)
print("--------------------")
print("Running SMA " + str(longWindow) + "/" + str(shortWindow) +
" Long/Short Crossover Strategy.")
print("--------------------")
startingBalance = 1000000
balance = startingBalance
unitsAvailable = 0
long = False
lastPrice = 0
stoploss = -1
entry = -1
stoplossPCT = .25
for i in range(1, count):
if long and dataframe.iloc[i]['close'] <= stoploss:
qty = math.floor(unitsAvailable * stoplossPCT)
print(
str(dataframe.iloc[i]["timestamp"]) +
": **PARTIAL STOP LOSS SELL** " + str(qty) + " at $" +
str(dataframe.iloc[i]['close']))
sys.stdout.flush()
balance += dataframe.iloc[i]['close'] * qty
unitsAvailable -= qty
if unitsAvailable == 0:
long = False
stoploss = stoploss - 0.05 * stoploss
continue
"""
if long and dataframe.iloc[i]['close'] >= 1.1*entry:
print (str(dataframe.iloc[i]["timestamp"]) + ": **PROFIT SELL** " + str(unitsAvailable) + " at $" + str(dataframe.iloc[i]['close']))
sys.stdout.flush()
long = False
balance += dataframe.iloc[i]['close'] * unitsAvailable
unitsAvailable = 0
continue
"""
if (dataframe.iloc[i][shortSMAColumn] >
dataframe.iloc[i][longSMAColumn]) and (
not long) and (dataframe.iloc[i - 1][shortSMAColumn] <
dataframe.iloc[i - 1][longSMAColumn]):
qty = math.floor(balance / dataframe.iloc[i]["close"])
print(
str(dataframe.iloc[i]["timestamp"]) + ": **BUY** " + str(qty) +
" units at $" + str(dataframe.iloc[i]["close"]))
sys.stdout.flush()
long = True
stoploss = dataframe.iloc[i][
"close"] - 0.05 * dataframe.iloc[i]["close"]
entry = dataframe.iloc[i]["close"]
#print("Setting STOP LOSS at " + str(stoploss))
sys.stdout.flush()
balance -= dataframe.iloc[i]["close"] * qty
unitsAvailable += qty
lastPrice = dataframe.iloc[i]["close"]
elif (dataframe.iloc[i][shortSMAColumn] <
dataframe.iloc[i][longSMAColumn]) and (
long) and (dataframe.iloc[i - 1][shortSMAColumn] >
dataframe.iloc[i - 1][longSMAColumn]
): # and dataframe.iloc[i]["close"] > lastPrice:
print(
str(dataframe.iloc[i]["timestamp"]) + ": **SELL** " +
str(unitsAvailable) + " at $" +
str(dataframe.iloc[i]["close"]))
sys.stdout.flush()
long = False
balance += dataframe.iloc[i]["close"] * unitsAvailable
unitsAvailable = 0
if unitsAvailable > 0:
balance += dataframe.iloc[i]["close"] * unitsAvailable
print(
str(dataframe.iloc[i]["timestamp"]) + ": **FORCE SELL** " +
str(unitsAvailable) + " at $" + str(dataframe.iloc[i]["close"]))
returnPercentage = 100 * ((balance - startingBalance) / startingBalance)
period = util.periods(dataframe) / 365
avgReturnPercentage = round(returnPercentage / period, 2)
period = round(period, 2)
returnPercentage = round(returnPercentage, 2)
balance = round(balance, 2)
print("\nBalance: " + str(balance) + "\nReturn Percentage: " +
str(returnPercentage) + "% over a period of " + str(period) +
" years.")
#print("Average Yearly Return Percentage: " + str(avgReturnPercentage) + "%")
print("Buy and Hold Return Percentage would have been: " + str(
round(
100 *
(dataframe.iloc[count - 1]['close'] - dataframe.iloc[0]['close']) /
dataframe.iloc[0]['close'], 2)) + "%")
print("I.e. Bought at $" + str(dataframe.iloc[0]['close']) + " on " +
str(dataframe.iloc[0]['timestamp'] + " and sold at $" +
str(dataframe.iloc[count - 1]['close']) + " on " +
str(dataframe.iloc[count - 1]['timestamp'])))
print("--------------------\n\n")
sys.stdout.flush()
