Ultimate Algorithmic Trading System

That’s it! These seven lines of code is all it took to create 1000 different and mostly
unique alternate trade histories. I won’t bore you with all of the details, but I will
step through the code quickly to demonstrate how easy this was to do with Python.
Initially I stored all of the trade dates and trade P/L in a tuple named tradeTuple.
From this list I culled all of the trades to fill up the 1000 alternate lists. Basically I
started with the source list and for each trade in the list I created a random number
between 0 and the number of trades:
randomTradeNum = random.randint(0,len(tradeTuple)-1)
Python generates random numbers through its module: random.randint. I then
used the randomTradeNum as an index into the tradeTuple to extract the
tradeDate and tradePL:
tradeDate = tradeTuple[randomTradeNum][0]
tradePL = tradeTuple[randomTradeNum][1]
Remember the first bracketed number following the tuple selects which tuple.
The second bracketed number selects the element in that particular tuple. In the
tradeTuple, [0] is the trade date and [1] is the trade P/L. While stepping through
the alternate histories sequentially, trades were randomly selected from the original
trade history and inserted. All of the alternate histories were then stored in a list
of tuples named mcTradeTuple. The += operand simply appends the current
alternate trade history to the list. In the end, you have one huge list that includes all
of the alternate histories.
AmiBroker’s Monte Carlo Simulation The Python Monte Carlo simulation is
included in the Python System Backtester. I programmed the simulation because I
wanted to fully understand the mechanism. Fortunately, you don’t need to program
these types of tools because they are usually included in your favorite testing platform.
This is the case for AmiBroker and TradeStation. AmiBroker’s Monte Carlo analysis
is so simple all you have to do is flip a switch. Click the Settings button in an Analysis
window and hit the Monte Carlo tab (see Figure 8.9). You have seen this dialog before
but this is the first time we have discussed the Monte Carlo settings. The default values
are the most popular. The position sizing parameters offers four different options:
(1) keep the sizing the same as the initial test, (2) use a fixed size of contracts or shares,
(3) use a fixed dollar value for size computation, and (4) use a percentage of equity in
fixed fractional approach. The fourth option can introduce serial dependency into the
Monte Carlo simulation due to the fact that the size of the current trade is dependent
upon the success of all of the prior trades. Utilizing this option might muddy the
waters a bit when trying to determine algorithm robustness. Before we move on
let’s discuss the idea of serial correlation among the trades of a trading algorithm.
This concept basically implies there is a relationship or connection between a trade
and the subsequent trade or trades. Critics of Monte Carlo simulation on actual
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GENETIC OPTIMIZATION, WALK FORWARD
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