Find Best Performing MA For Golden Cross

Hello!
This script calculates the performance of any asset following a golden cross of two moving averages of any length!
The calculated moving averages are: SMA, EMA, HMA, VWMA, WMA, LSMA, and ALMA

The best performing moving average for the selected data series is listed first, followed by a descending order.

The indicator works on any timeframe, any asset, and can even be used on indicators such as RSI, %b, %k, etc.

The Moving Average Length and Source Are Customizable!

The Moving Averages Can Be Plotted on Most Data Series, Such As:
Close, Open, Low, hlc3, RSI, %B, %K, Etc.

The Script Will Recalculate for the Timeframe (1m, 5m, D, etc.)!

The (XX Candles) Indicates the Average Number of
Sessions the Shorter Ma Remains Above the Longer Ma Following an Upside Cross!

The Percentages (XX.XX%) Indicate the Average
Percentage Price Gain/Loss Following a Golden Cross,
Until the Shorter Ma Crosses Back Under the Longer Ma!

In This Example I Am Using a 63 Session Length for the
Shorter Ma for All Listed Ma Types for Closing Prices, and a 196 Candle Length for the Longer Ma!

Notes de version:

I am posting a quick update to outline the advantages and disadvantages of indicator/strategy optimization!

An optimization script is best implemented to discover what won't work, not what will work. The best performing "optimized" parameters are not a guaranteed profitable investment system. While we may see an exceptionally positive performance for a set of parameters, it's impossible to know how much of that performance is the beneficiary of market noise in the absence of additional testing. Most market moves are noise - irreplicable sequences that offer no predictive utility - and most "good" backtests overwhelmingly benefit from these irreplicable sequences. An investor unfamiliar with this concept may be lead to believe they have found a valid correlation between an indicator sequence and subsequent price movement, despite the correlation being illusory.

Consequently, it should be assumed that the best performing parameters strongly benefitted from market noise and will not work in a live market - until further rigorous statistical tests are performed on an investment system built around the best performing parameters. This includes out-of-sample, in-sample, and forward testing in addition to testing negatively correlated, positively correlated and zero-correlation assets; testing additional assets should be treated as prerequisite to live implementation.

Of course, all trading strategies, even one's that methodically exploit a valid correlation/replicable sequence, will benefit from market noise - it's impossible to avoid. However, a "legit" trading strategy has a chance to work on future price data, while an overoptimized strategy will fail miserably on new price data!

This script tests 102 parameter combinations concurrently and returns the performances in descending order. An overoptimized strategy is virtually guaranteed to have a better backtest performance than a valid strategy. The overoptimized strategy will fail in a live market while the valid strategy has a chance of working. So, should you notice the best performing SuperTrend parameters, be sure to build a comprehensive trading system around the parameters and perform additional tests. This is the only way to know if the optimized parameters will truly work in a live market!

Unfortunately, they often will not!

Notes de version:

Memory calculation compliant