u/therealjameskirk

I’ve been building a Python/MT5 live-forward FX research engine that runs as a paper-only shadow execution layer.

The core idea is to separate signal discovery from execution risk. Instead of testing one setup at a time, the engine monitors live broker ticks across a basket of FX pairs, detects statistically abnormal price-dislocation events, and opens virtual trades across a matrix of entry models. No orders are sent to the broker.

The system currently evaluates:

• Rolling tick-based z-score dislocation events
• Multi-pair FX scanning through MT5
• Long-side mean-reversion and continuation filters
• RSI / Bollinger / EMA / ADX / MACD / stochastic / MFI conditions
• Multi-timeframe confirmation logic
• Candle-structure and volatility filters
• Tick-VWAP reclaim logic
• Broker-dependent futures/proxy VWAP confirmation where symbols are available
• Currency-strength style relative-momentum filters
• Geometry/volatility displacement models

The more important part is the exit/risk layer. Every virtual entry is scored against an exit matrix instead of one hard-coded exit.

The engine compares:

• Fixed-time exits
• Multiple trailing-stop configurations
• Breakeven-plus-trailing models
• Z-score mean-reversion exits
• RSI exhaustion exits
• Bollinger mid/upper-band exits
• Sharpe, CAGR & Max Drawdown
• EMA failure exits
• MACD reversal exits
• Hard stop-loss variants
• ATR-based stop-loss variants
• Stop-protected versus unprotected results

For each virtual trade, it tracks max favorable excursion, max adverse excursion, stop-out behavior, missed-pips, capture rate, exit reason, realized pips, cash estimate, and time-in-trade. Summary rankings include win rate, average pips, total pips, estimated cash, stop-out rate, Sharpe estimate, CAGR estimate, and max drawdown estimate.

The point is not to claim profitability from a short sample. The point is to collect live-forward evidence and isolate where the edge actually exists: entry condition, exit logic, stop placement, or market regime.

It is basically a research harness for answering:

1. Which setups produce forward movement after live spread?
2. Which exits capture the move instead of cutting winners short?
3. Which strategies survive realistic stop-outs?
4. Which combinations still rank well after drawdown and risk metrics?

Still early-stage, but the architecture is getting interesting.

This can be updated/edited for stocks or crypto. And then all you need to do is add your strategies and run the python script, thats it!

#2 screenshots are v.3

#1 screenshot is v.4 and I just kicked off so after 60 minutes it will start populating all of the data. v4 includes vwap tick and futures, cadr, sharpe, max-drawdown and more..