No, it's not french and it's not the movie.

It's a fast-N-rough "

Let's assume we want to trade mean-reversion: If price moves down we buy, if it moves up we sell.

Possible Indicators from the blog-o-sphere:

RSI(2),RSI(3),RSI(4)

DV2 here and here

BSI here or here

BoilingerBands

CRSI here

TD9 here

One way to choose an indicator is to run backtests for each one (and each one's parameters) and pick the one that has performed the best in the recent past. "Performance" can be pure profit or it can be any other metric we choose: CAR/MDD (annual return/MaxDrawdown), Profit Factor, Expectancy or your very own "Bliss function".

So if DV2 has performed best in the past 6 months, maybe we should use that to trade for the next few weeks.

Just one instrument?

Why not backtest all indicators on all instrument and let the data tell us which instrument behaves best with which indicator? We can then trade that one. Did someone say "overfit"?.

Why not a bunch of instruments? A portfolio! Yes and we 'll throw some weights in there, too:

30% SPY 10% AAPL 10% XLU 20% IBM 30% GLD ... wightN*InstrumentN

I guess now we have to backtest the different indicators with these different weights on our instruments, too. What are we doing here? Let's step back.

We start with a set of selected assets (StockA, StockB,... StockN).

We bring in a set of selected indicators (ind1, Ind2, ...IndN).

We then build an "ammi" that picks and chooses from these two universes.

We optimize that "ammi" using some type of non-exhaustive optimization technique.

In other words we are creating a portfolio and trading each instrument inside the portfolio with each own indicator/strategy. So what's the catch? There's nothing new here. We could just trade SPY with DV2 and AAPL with RSI(3). Allocate 30% of capital to strategy "SPY" and 10% to strategy "AAPL". Is there an advantage in mixing it all up?

For the purposes of this post we will assume:

1. Our optimizing target is % profit.

2. We are investing a fixed percentage of current equity on each trade (i.e., 10% of Equity). In other words we are compounding.

By optimizing one instrument and one strategy, the optimizer will look for the most consistent "timing" to enter and exit trades.

By optimizing the whole AMMI, the optimizer is handed one more tool. Since it wants to maximize compound profit it can do so by minimizing max drawdown which in turn gets accomplished by minimizing correlations between the "weighted and traded" equity curves. So, in theory, the optimizer should not necessarily choose the best

Almost forgot! We also want to be able to change both the "instrument" and the "strategy" as time passes. That means we re-optimize every x days and trade with the fresh settings. Hence, the "

For the sake of simplicity, we will consider indicators RSI(2), RSI(3) and RSI(4). Possible thresholds are 0-->50 for a Buy signal and 50-->100 for a Sell signal. We will use 10 instruments that can have weights ranging from 5% --> 100%. We assume the standard 2x leverage can be used.

Let's run an Out Of Sample test. In_Sample (IS) Optimization period is 2 years, Out_Of_Sample trading period (OOS) is 3 months.**

Optimization is done using Amibroker's non-exhaustive CMAE plug-in set to run very few "runs" so as not to overfit (and not wait too long...). Target for the optimization is pure % Profit (hence the huge draw-downs).

Here's the result trading a few instruments and using RSI(2), RSI(3) and RSI(4) as possible indicators with varied thresholds. This is an OOS (out of sample, i.e., realistic) equity. At 40%+ (in the "good" version) draw-downs, it's not something I would trade***. It's just for illustration purposes.

Again. this is a rough "model". It is best used as a starting point to get ideas going. It might also help to get a sense of how different data groups react to different indicators. Instead of mean reverting indicators you could have trend following , or pattern based indicators. And instead of optimizing precise values you could fuzzify them.

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* Adding a losing strategy to a profitable strategy may increased the Sharpe Ratio of the system.

**The In-Sample optimization will gives us the weights on the instruments as well as the indicator and the thresholds that were used to produce the best performance. An example would be 5% InstrA, 0% InstrB, 0%InsrC,70% InstrD,100% InstrE, 25% InstrF, etc. Indicator would be RSI(4) and lower and upper thresholds would be 30 and 70. Those set of parameters whould then be used to trade "live" (Out-Of-Sample) for the next three months. After that we would re-optimize (inclusive of the 3 months that were traded) and change param for the next 3 months.

***Unless you know what you are doing, optimizing a system for Max Profit, is a fairly bad idea. It will tend to pick few winners and not diversify (i.e., in hindsight it will tend to pick 100% APPLE).

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The code in Amibroker:

```
//----Code by Sanz P.-----------------------------------------------------//
//----Place the instruments on a watchlist and select it under Parameters
//----Optimize/Walk Forward on that watchlist
//------------------------------------------------------------------------//
function Set2D( tablename, x, y, value )
{VarSet( tablename + StrFormat("%03.0f%03.0f", x, y ), value );}
function Get2D( tablename, x, y )
{return VarGet( tablename + StrFormat("%03.0f%03.0f", x, y ) );}
OptimizerSetEngine("cmae");
OptimizerSetOption("Runs", 3 );
OptimizerSetOption("MaxEval", 300 );
WatchlistNumber =Param("Choose Watchlist with Tickers",12,0,30,1);
TickerList= CategoryGetSymbols( categoryWatchlist , WatchlistNumber ) ;
tablename="a";
//init
for( n=0; (instrument=StrExtract( TickerList, n)) != ""; n++)
set2d( tablename, n, 0, n );
Buy=Sell=0;
for( n=0; (instrument=StrExtract( TickerList, n)) != ""; n++)
{
set2d( tablename, n, 1 , Optimize("Ticker"+n+ "Position",20,0,50,5) ); //pos
set2d( tablename, n, 2 , Optimize("Ind"+n,2,0,2,1) ); //ind
set2d( tablename, n, 3 , Optimize("Thrsh"+n,20,0,100,5) ); //thr
}
for( n=0; (instrument=StrExtract( TickerList, n)) != ""; n++)
{
if (Name()== instrument)
{
indicator=0;//init
indicatorcode=get2d( tablename, n, 2 );
switch(indicatorcode)
{
case 0:
indicator=RSI(2);
break;
case 1:
indicator=RSI(3);
break;
case 2:
indicator=RSI(4);
break;
}
PositionSize= - get2d( tablename, n, 1 );
thresh=get2d( tablename, n, 3 );
Buy=Cross(thresh,indicator);
Sell=Cross(indicator,thresh);
}
}
SetTradeDelays(1,1,1,1);
BuyPrice=SellPrice=ShortPrice=CoverPrice=O;
SetOption("MaxOpenPositions",10);
```

Labels: adaptive, Amibroker, backtest, BSI, DV2, instrument, mean reversion, multi, RSI2, strategy, walk forward