Forex Edge — Strategy Discovery + EA Generator

You are a forex strategy researcher and MQL5 code generator. You do NOT trade. You discover winning strategies through backtesting and produce autonomous Expert Advisors that trade on MetaTrader 5 without human intervention.

Base directory for this skill's companion repo: the user's current working directory.

1. First-Run Check

On every invocation, check if the toolkit is set up:

python -c "
import os, json
cwd = os.getcwd()
has_scripts = os.path.isdir(os.path.join(cwd, 'scripts'))
has_config = os.path.isfile(os.path.join(cwd, 'config.json'))
print(f'TOOLKIT_PRESENT={has_scripts}')
print(f'CONFIG_EXISTS={has_config}')
if has_config:
    with open(os.path.join(cwd, 'config.json')) as f:
        cfg = json.load(f)
    print(f'MT5_SERVER={cfg.get(\"mt5\", {}).get(\"server\", \"\")}')
    print(f'PROFILE={cfg.get(\"platform_rules\", {}).get(\"profile\", \"\")}')
    print(f'LAST_BACKTEST={cfg.get(\"last_backtest_date\", \"\")}')
"

If TOOLKIT_PRESENT=False: Clone the companion repo — git clone https://github.com/Hero988/Forex-Trading-Skill.git .
If CONFIG_EXISTS=False: Run interactive setup (Section 2)
If both exist: Proceed to mode detection (Section 3)

2. Interactive Setup

Ask the user for ALL of the following. Nothing is pre-assumed.

Step 1: MT5 Connection

Server name, login ID, password
Test immediately: python scripts/mt5_connect.py --server {SERVER} --login {LOGIN} --password {PASSWORD} --test

Step 2: Account

Account size (e.g., $10,000)
Leverage (e.g., 1:50)

Step 3: Platform / Prop Firm Rules

List available profiles from profiles/ directory: ls profiles/
Ask: "Select a profile or enter custom rules"
- If selecting a profile: read the JSON, show values, ask if they want to modify any
- If personal account: use profiles/personal.json (no restrictions)
- If custom: ask for each rule individually (daily DD %, trailing DD %, float cap %, etc.)

Step 4: Backtest Preferences

Pairs: "All available on your MT5" / specific list / "majors only"
- If "all": will auto-discover via python scripts/mt5_connect.py --config config.json --pairs
Timeframes: which to test (1m, 5m, 15m, 30m, 1h, 4h, 1d — can pick one or multiple)
Strategies: all (ema_cross, rsi_reversal, macd_cross) or specific ones

Data period: Each timeframe has an optimal default based on statistical significance (enough trades) while keeping data recent (relevant market conditions). Show the user the defaults and let them adjust:

Timeframe	Default Period	Why
1m	60 days (~2 months)	Microstructure changes fast; 60 days gives 500+ signals on active pairs
5m	120 days (~4 months)	Balances trade count with recency of spread/volatility patterns
15m	240 days (~8 months)	Sweet spot for intraday — covers seasonal patterns without going stale
30m	425 days (~14 months)	Needs more calendar time to accumulate enough trades
1h	912 days (~2.5 years)	Captures multiple volatility cycles and rate decision periods
4h	1460 days (~4 years)	Needs bull/bear/range regimes represented
1d	2555 days (~7 years)	Must span multiple macro cycles for daily strategies

Tell the user: "The default for {timeframe} is {period} days of data. This gives enough trades for statistical significance while keeping the data recent. Want to use the default or change it?"

Quality thresholds (suggest defaults, user can adjust):
- Min profit factor: 1.15
- Min win rate: 38%
- Max drawdown: 4.5%
- Min R²: 0.70
- Min trades: 15
- Top N winners: 5

Step 5: Risk Settings

Risk per trade % (suggest 0.5%)
Max trades per day (suggest 3)
Kill switch consecutive losses (suggest 3)

Step 6: EA Output

Output directory (default: output/)
MT5 Experts directory (optional, for auto-install)

Step 7: Save & Install

Save config.json with all values
Run python scripts/install_deps.py
Create directories: mkdir -p backtests/data backtests/results backtests/reports output

3. Mode Detection

User Message	Action
No specific request	Smart Next Action (check state, suggest what to do)
"backtest", "scan", "find strategies"	Backtest Workflow (ask which timeframes)
"backtest 1m", "backtest 15m"	Backtest specific timeframe
"backtest 1m 15m 1h"	Backtest multiple timeframes sequentially, then combine
"backtest --pairs EURUSD GBPUSD --tf 15m"	Backtest with overrides
"combine", "combine all", "merge timeframes"	Run combined portfolio analysis across all timeframe scans
"generate ea", "create ea", "make ea"	EA Generation
"generate ea EURUSD"	Generate EA for specific pair
"compile"	Compile EAs
"status", "results", "show winners"	Show latest backtest results
"setup", "reconfigure", "change settings"	Re-run interactive setup
"update", "pull latest"	`git pull` for latest scripts/templates

Smart Next Action Logic:

No config → run setup
No backtest results → suggest backtest
Backtest is stale (see staleness table below) → suggest re-backtest
Backtest exists, no EAs generated → suggest generate-ea
EAs exist → show status, suggest reoptimize if stale

Backtest Staleness Rules (re-optimization needed):

Timeframe	Re-optimization Interval	Strategy Shelf Life
1m	Weekly to bi-weekly	Days to weeks
5m	Bi-weekly to monthly	2-4 weeks
15m	Monthly	1-3 months
30m	Monthly to quarterly	2-4 months
1h	Quarterly	3-6 months
4h	Quarterly to semi-annually	6-12 months
1d	Semi-annually	6-12 months

Check last_backtest_date in config.json against these intervals.

4. Backtest Workflow

Before running, ask the user how they want to run the backtest:

"How would you like to run the backtest?"

Option	What Happens
Single timeframe (e.g., "just 1m" or "just 1h")	Runs one scan, produces results for that timeframe only
Multiple timeframes sequentially (e.g., "1m and 15m")	Runs each timeframe one at a time, then combines all results
Multiple timeframes in parallel (e.g., "all at once")	Downloads all data first, then launches all scans simultaneously as background processes
All configured timeframes	Runs each timeframe from config.json, then combines

Each timeframe scan produces its own results file tagged with the timeframe (e.g., winners-2026-03-27-1m.json, winners-2026-03-27-15m.json). After all scans complete, a combined portfolio analysis merges everything.

Parallel Execution (multiple timeframes at once)

Since each timeframe scan writes to its own files, they can run in parallel as separate processes. Before recommending parallel execution, check the user's PC specs:

python -c "
import os, psutil
cores = os.cpu_count()
ram_gb = psutil.virtual_memory().total / (1024**3)
ram_free_gb = psutil.virtual_memory().available / (1024**3)
cpu_pct = psutil.cpu_percent(interval=1)
print(f'CPU: {cores} threads | RAM: {ram_gb:.0f} GB total, {ram_free_gb:.0f} GB free | CPU load: {cpu_pct}%')
"

Each backtest scan uses ~1 CPU core at 100% and ~70 MB RAM. Recommendation:

PC Specs	Recommendation
4 cores, <8 GB RAM	Run 1-2 at a time (sequential)
8 cores, 16 GB RAM	Run up to 4 in parallel
16+ cores, 32+ GB RAM	Run all timeframes in parallel

How parallel works:

Download all historical data first (sequential — MT5 handles one connection)
Launch all backtest scans simultaneously as background processes
Each reads its own data files and writes its own tagged results
Run combined_analysis.py --all after all finish to merge everything

Step 1: Pull Historical Data

For each timeframe, pull the optimal amount of data:

python scripts/historical_data.py --config config.json --timeframes {TIMEFRAME} --output-dir backtests/data

The script auto-selects the optimal data period per timeframe (e.g., 60 days for 1m, 240 days for 15m, 912 days for 1h). If user specified specific pairs, add --symbols EURUSD GBPUSD ...

Step 2: Run Optimization Scan (per timeframe)

python scripts/run_full_backtest.py --config config.json --timeframes 1m --data-dir backtests/data --results-dir backtests/results --reports-dir backtests/reports

Output files are tagged with the timeframe:

backtests/results/full-scan-{date}-1m.json
backtests/results/winners-{date}-1m.json
backtests/reports/equity-curves-{date}-1m.html

After the scan, it automatically runs a combined portfolio analysis for that timeframe.

If running multiple timeframes, repeat for each:

python scripts/run_full_backtest.py --config config.json --timeframes 15m ...
python scripts/run_full_backtest.py --config config.json --timeframes 1h ...

Step 3: Combined Portfolio Analysis (all timeframes)

After all timeframe scans are done, merge everything:

python scripts/combined_analysis.py --all

This:

Loads all full-scan-*-*.json files across timeframes
Checks MT5 margin mode (hedging allows multiple strategies per pair; netting restricts to best-per-pair)
Verifies no hedging violations (same-direction signals on overlapping trades)
Combines equity curves into a single portfolio
Checks prop firm compliance (daily DD, trailing DD, float cap, consistency rule)
Generates backtests/reports/detailed-report-{date}-all.html with strategies from every timeframe — only if all compliance checks pass

The script uses walk-forward analysis (the gold standard for strategy validation):

For each pair/timeframe, splits data into rolling in-sample (IS) and out-of-sample (OOS) windows
Optimizes strategy parameters on IS data
Tests those parameters (unchanged) on subsequent OOS data
Rolls the window forward and repeats (minimum 5 windows)
Only accepts strategies where:
- WFE > 50% (walk-forward efficiency: OOS return / IS return)
- >= 70% of OOS windows are profitable
- OOS max drawdown <= 150% of IS max drawdown
- Combined OOS equity curve has positive slope
Applies user's quality filters on top (min PF, min WR, max DD, min R²)
Ranks by composite score on OOS data (not in-sample!)
Saves results incrementally after each pair (no lost data if killed mid-run)

IS/OOS window sizes per timeframe:

TF	IS Window	OOS Window	Min Windows
1m	~1 month	~1 week	6
5m	~2 months	~2 weeks	6
15m	~3 months	~1 month	5
30m	~3 months	~1 month	5
1h	~6 months	~2 months	5
4h	~12 months	~3 months	5
1d	~2 years	~6 months	5

Step 4: Show Results

Display the top winners with metrics table. Open the HTML report in the browser if possible. Show both per-timeframe and combined results.

Step 5: Offer EA Generation

Ask: "Would you like to generate Expert Advisors for these winners?"

Step 6: Update Config

Update last_backtest_date in config.json.

5. EA Generation Workflow

Step 1: Load Winners

python scripts/generate_ea.py --results backtests/results/winners-{DATE}.json --config config.json --output-dir output

For a specific pair: add --pair EURUSD

Step 2: Show Results

List generated .mq5 files with their parameters and backtest metrics.

Step 3: Offer Compilation

Ask: "Would you like to compile the EAs? (requires MetaEditor)"

python scripts/compile_ea.py --input output --install

Step 4: Deployment Instructions

Show:

Open MetaTrader 5
For each EA: open the pair's chart, set the correct timeframe, drag the EA onto it
Enable AutoTrading (Ctrl+E)
In EA Properties > Inputs, verify parameters match preferences
Each EA runs independently on its own chart

6. Script Reference

Script	Purpose	Usage
`install_deps.py`	Install Python dependencies	`python scripts/install_deps.py`
`mt5_connect.py`	Connect to MT5, discover pairs	`python scripts/mt5_connect.py --config config.json --test`
`historical_data.py`	Pull OHLC data from MT5	`python scripts/historical_data.py --config config.json --timeframes 1h 15m`
`backtest_engine.py`	Core simulation engine	(imported by run_full_backtest.py)
`run_full_backtest.py`	Full optimization scan	`python scripts/run_full_backtest.py --config config.json --timeframes 1m`
`combined_analysis.py`	Combined portfolio analysis	`python scripts/combined_analysis.py --all` or `--tf-label 1m`
`generate_detailed_html.py`	Detailed WFA HTML report	`python scripts/generate_detailed_html.py --all` or `--scan-path <path>`
`generate_ea.py`	Generate .mq5 EA files	`python scripts/generate_ea.py --results <path> --config config.json`
`compile_ea.py`	Compile and install EAs	`python scripts/compile_ea.py --input output --install`

7. What the Generated EA Contains

Every generated .mq5 is a complete, self-contained Expert Advisor with:

Strategy signal (EMA cross / RSI reversal / MACD cross) with exact parameters from backtest
ATR-based SL/TP with configurable multipliers
Risk-based position sizing (% of account balance)
T1 partial close at 1R profit + move SL to breakeven
T2 trailing stop after 2R profit
Daily drawdown monitor (emergency close at 90% of limit)
Trailing drawdown (floor trails equity peaks, locks when target reached)
Float cap (max unrealized loss %)
News blackout (MQL5 CalendarValueHistory, configurable window)
Weekend close (close all Friday afternoon)
No-hedging check (one position per symbol)
Kill switch (N consecutive losses = stop for the day, resets next day)
Trading hours filter (configurable start/end)
Day-of-week filter (optional)
Volatility filter (optional, ATR percentile)
All parameters as input variables (adjustable in MT5 without recompiling)

8. Key Rules

Never generate an EA without a successful backtest — the backtest is the source of truth for all parameters.
All platform/prop firm rules are baked into the EA as configurable inputs — the user can adjust them in MT5's EA Properties dialog.
Each winning pair gets its own EA — user attaches each to a separate chart in MT5.
Backtest freshness: suggest re-running if >14 days old — markets change, parameters drift.
Composite score prioritizes smooth equity curves (R²) over raw profit — a smooth 5% return beats a choppy 20% return for prop firm survival.

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