It simulates an automated trading strategy against a simulated stock.
I think the results are interesting. It seems number of wins are always higher than number of losses but average amount of loss is also always higher than average amount of win!
Python, 62 lines
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# FB - 20140515
import random
initialMoneyOwned = 1000.0
initialStocksOwned = 0.1
initialStockPrice = 10000.0
tradingDays = 1000
tp = 5.0 # buy/sell percentage threshold of the investor
maxVolatilityPercent = 5.0 # of the stock
numTrials = 1000
initialInvestment = initialMoneyOwned + initialStocksOwned * initialStockPrice
def SimulateTrading(moneyOwned, stocksOwned, stockPrice, days):
stockBuySellPrice = stockPrice
for day in range(days):
volatility = random.random() * stockPrice * maxVolatilityPercent / 100.0
stockPrice += (random.random() * 2.0 - 1.0) * volatility
# trading
if stocksOwned > 0.0:
if stockPrice >= stockBuySellPrice * (100.0 + tp) / 100.0:
# sell
moneyOwned += stocksOwned * stockPrice
stocksOwned = 0.0
stockBuySellPrice = stockPrice
if moneyOwned > 0.0:
if stockPrice <= stockBuySellPrice * (100.0 - tp) / 100.0:
# buy
stocksOwned += moneyOwned / stockPrice
moneyOwned = 0.0
stockBuySellPrice = stockPrice
return (moneyOwned, stocksOwned, stockPrice)
numWins = 0
numLosses = 0
totalWins = 0.0
totalLosses = 0.0
for i in range(numTrials):
(moneyOwned, stocksOwned, stockPrice) = \
SimulateTrading(initialMoneyOwned, initialStocksOwned, initialStockPrice, tradingDays)
finalReturn = moneyOwned + stocksOwned * stockPrice - initialInvestment
if finalReturn > 0.0:
numWins += 1
totalWins += finalReturn
elif finalReturn < 0.0:
numLosses += 1
totalLosses += finalReturn
print "Initial Investment = " + str(initialInvestment)
print "Trading Days = " + str(tradingDays)
print "Number of Trials = " + str(numTrials)
print "Number of Wins = " + str(numWins)
print "Average Win Amt = " + str(totalWins / numWins)
print "Number of Losses = " + str(numLosses)
print "Average Loss Amt = " + str(abs(totalLosses / numLosses))
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