MetaTrader5 MCP Server

# End-to-End Trading Workflow Example **Related:** - [SETUP.md](SETUP.md) — Installation and MT5 connection - [CLI.md](CLI.md) — CLI usage patterns and output formats - [SAMPLE-TRADE.md](SAMPLE-TRADE.md) — Guided workflow (start here if new) This walkthrough shows a practical “research loop”: discover methods, fetch data, add context (indicators/denoise), generate forecasts, size risk (volatility + barriers), and validate with a quick backtest. Assumptions: - MetaTrader 5 terminal is running. - You call tools via `python cli.py <command> ...`. - Replace `EURUSD` and `H1` with your symbol/timeframe. Tip: add `--format json` for structured output. --- ## 1) Discover what’s available Forecast methods + availability (optional libraries show up here): ```bash python cli.py forecast_list_methods --format json ``` Forecast model spaces: ```bash python cli.py forecast_list_library_models native --format json python cli.py forecast_list_library_models statsforecast --format json python cli.py forecast_list_library_models sktime --format json python cli.py forecast_list_library_models pretrained --format json ``` Indicators: ```bash python cli.py indicators_list --limit 50 python cli.py indicators_describe rsi --format json ``` --- ## 2) Inspect symbol and fetch candles ```bash python cli.py symbols_list --limit 20 python cli.py symbols_describe EURUSD --format json python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 300 --format json ``` Optional (liquidity snapshot): ```bash python cli.py market_depth_fetch EURUSD --format json ``` --- ## 3) Prepare data: indicators + denoise Compute a few indicators: ```bash python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 1500 \ --indicators "ema(20),ema(50),rsi(14),macd(12,26,9)" \ --format json ``` Denoise before indicators (smoother inputs): ```bash python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 1500 \ --indicators "rsi(14),ema(50)" \ --denoise ema --denoise-params "columns=close,when=pre_ti,alpha=0.2,keep_original=true" \ --format json ``` Denoise after indicators (smoother signals): ```bash python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 1500 \ --indicators "rsi(14)" \ --denoise ema --denoise-params "columns=RSI_14,when=post_ti,alpha=0.3,keep_original=true" \ --format json ``` --- ## 4) Baseline price forecasts Simple baseline (Theta): ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 \ --library native --model theta --format json ``` Pattern-based “analog” forecast (nearest-neighbor style): ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 \ --library native --model analog --model-params "window_size=64 search_depth=5000 top_k=20 scale=zscore" \ --format json ``` Optional: foundation model (if available): ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 24 \ --library pretrained --model chronos2 --model-params "context_length=512" \ --format json ``` Optional: Monte Carlo simulation forecast (range of outcomes): ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 \ --library native --model mc_gbm --model-params "n_sims=3000 seed=7" \ --format json ``` --- ## 5) Volatility forecast (risk sizing) EWMA (fast default): ```bash python cli.py forecast_volatility_estimate EURUSD --timeframe H1 --horizon 12 \ --method ewma --params "lambda=0.94" --format json ``` HAR-RV (uses intraday realized volatility): ```bash python cli.py forecast_volatility_estimate EURUSD --timeframe H1 --horizon 12 \ --method har_rv --params "rv_timeframe=M5,days=150,window_w=5,window_m=22" --format json ``` --- ## 6) Barrier analytics (TP/SL planning) Barrier probabilities (TP/SL odds within the horizon): ```bash python cli.py forecast_barrier_prob EURUSD --timeframe H1 --horizon 12 \ --method mc --mc-method hmm_mc --tp-pct 0.5 --sl-pct 0.3 --params "n_sims=5000 seed=7" \ --format json ``` Barrier optimization (search a TP/SL grid): ```bash python cli.py forecast_barrier_optimize EURUSD --timeframe H1 --horizon 12 \ --method hmm_mc --mode pct --grid-style volatility --refine true \ --tp-min 0.25 --tp-max 1.5 --tp-steps 7 \ --sl-min 0.25 --sl-max 2.5 --sl-steps 9 \ --params "n_sims=5000 seed=7" --format json ``` Interpretation shortcuts: - `edge` ≈ P(TP first) − P(SL first) - `prob_resolve` ≈ 1 − P(no hit) - Wider stops/targets may look “worse” on edge but can be better after accounting for time-to-hit and payoff ratio. --- ## 7) Regime detection (gatekeeper) Change-points (BOCPD): ```bash python cli.py regime_detect EURUSD --timeframe H1 --limit 1500 \ --method bocpd --threshold 0.6 --output summary --lookback 300 --format json ``` Regime labels (HMM): ```bash python cli.py regime_detect EURUSD --timeframe H1 --limit 1500 \ --method hmm --params "n_states=3" --output compact --lookback 300 --format json ``` --- ## 8) Quick backtest (sanity check) Run a rolling-origin backtest to compare a few methods: ```bash python cli.py forecast_backtest_run EURUSD --timeframe H1 --horizon 12 \ --steps 50 --spacing 5 --methods "theta sf_autoarima analog" --format json ``` --- ## 9) Pattern detection (optional context) Candlestick patterns: ```bash python cli.py patterns_detect EURUSD --timeframe H1 --mode candlestick --limit 500 \ --robust-only true --format json ``` Classic chart patterns: ```bash python cli.py patterns_detect EURUSD --timeframe H1 --mode classic --limit 800 --format json ``` --- ## 10) Combine signals into a decision (simple recipe) A pragmatic decision checklist: - Forecast direction: do multiple methods agree on the sign of the next `horizon` bars? - Volatility: is projected volatility within your risk budget? - Regime: are you trading in a regime your strategy is designed for? - Barriers: does the TP/SL pair have acceptable `edge` / `prob_resolve`? - Patterns: do detections support or contradict the idea (optional)? --- ## Appendix: Troubleshooting See [TROUBLESHOOTING.md](TROUBLESHOOTING.md).