MetaTrader5 MCP Server

## Advanced Forecast‑to‑Trade Playbook (EURUSD, H1) This guide extends the basic workflow with regime filters, conformal intervals, realized‑volatility (HAR‑RV), Monte‑Carlo barrier analytics, and disciplined risk/execution controls. It is designed to be modular: run each block, inspect outputs, and gate the next step by thresholds you calibrate via backtests. Assumptions - Horizon: 12 H1 bars (≈ half‑day) - Symbol/TF: EURUSD/H1 - All commands are runnable via `python cli.py <tool> ... --format json` --- ### 0) Safety & Hygiene - Skip high‑impact news windows (e.g., ±60 minutes of CPI/NFP/FOMC). - Enforce daily loss cap (e.g., 1–2× average daily VaR) and per‑trade risk cap (e.g., 0.25–1.0% equity). - Consider minimum spread/liquidity filter (e.g., spread < 1.5× median) before entry. --- ### 1) Regime & Break Detection (Gatekeeper) Detect structural breaks and label regimes so you avoid trading through hostile phases. 1.1 BOCPD change‑points (returns) ```bash python cli.py regime_detect EURUSD --timeframe H1 --limit 1500 \ --method bocpd --threshold 0.6 --output summary --lookback 300 --format json ``` - Gate: if `max(cp_prob[-24:]) >= 0.6` → stand down or reduce size; retrain/recalibrate models. 1.2 HMM‑lite regimes (returns) ```bash python cli.py regime_detect EURUSD --timeframe H1 --limit 1500 \ --method hmm --params "n_states=3" --output compact --lookback 300 --format json ``` - Derive a simple regime tag: {trend‑lowvol, trend‑highvol, range} from `state` and `state_probabilities`. - Gate: trade only when regime in {trend‑lowvol, trend‑midvol}; reduce risk in range/highvol. Optional: MS‑AR(1) (statsmodels) ```bash python cli.py regime_detect EURUSD --timeframe H1 --limit 1500 \ --method ms_ar --params "k_regimes=2 order=1" --output summary --format json ``` --- ### 2) Realized Volatility & Risk Budget (HAR‑RV) Estimate daily realized variance from intraday returns, then map to H1. ```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 ``` - Extract `sigma_bar_return` (per‑bar σ) and `horizon_sigma_return` (k‑bar σ). - Risk budget: set per‑trade risk ≤ min(0.7× daily VaR, fixed cap). Use σ to set realistic TP/SL and lot size. --- ### 3) Denoise + Quick Technical Context Pull data with light denoising and a few TIs for situational awareness (no heavy feature stacks in this flow). ```bash python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 300 \ --indicators "ema(20),ema(50),rsi(14),macd(12,26,9)" \ --denoise ema --denoise-params "columns=close,when=pre_ti,alpha=0.2,keep_original=true" --format json ``` - Context: price vs EMA(20/50), RSI near extremes, MACD momentum slope. - Gate: prefer longs if price > EMA(20)>EMA(50) and regime=trend. --- ### 4) Forecast with Valid Intervals (Conformal) Calibrate per‑step residual quantiles via rolling backtest; then get point + conformal bands. ```bash python cli.py forecast_conformal_intervals EURUSD --timeframe H1 --method fourier_ols \ --horizon 12 --steps 25 --spacing 10 --alpha 0.1 --format json ``` - Use `lower_price`/`upper_price` (conformal), not model CIs, for entry gating and sizing. - Gate: only take longs if `conformal.lower_price[h-1] > pivot` and point forecast trend=up. --- ### 5) Barrier Analytics (MC + Closed‑Form) 5.1 Optimize TP/SL grid with HMM MC paths ```bash python cli.py forecast_barrier_optimize EURUSD --timeframe H1 --horizon 12 \ --method hmm_mc --mode pct --grid-style volatility --refine true --refine-radius 0.35 \ --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" --top-k 5 --return-grid false --output summary --format json ``` - Choose a combo by objective (edge/Kelly/EV) subject to constraints: - Min edge ≥ 0.10; prob_no_hit not excessive; median time‑to‑TP ≤ horizon/2. 5.2 TP/SL odds for the chosen combo ```bash python cli.py forecast_barrier_hit_probabilities EURUSD --timeframe H1 --horizon 12 \ --method hmm_mc --tp_pct 0.4 --sl_pct 0.8 --params "n_sims=5000 seed=7" --format json ``` 5.3 Closed‑form GBM sanity check (fast) ```bash python cli.py forecast_barrier_closed_form EURUSD --timeframe H1 --horizon 12 \ --direction up --barrier 1.1795 --format json ``` - Flag discrepancies (e.g., MC>>GBM) to reduce size or re‑check calibration. --- ### 6) Labeling & Threshold Calibration (Optional but Recommended) Use triple‑barrier labels offline for signal evaluation and meta‑models. ```bash python cli.py labels_triple_barrier EURUSD --timeframe H1 --limit 2000 \ --horizon 12 --tp_pct 0.4 --sl_pct 0.8 --label-on high_low \ --output summary --lookback 300 --format json ``` - Compute in‑sample precision/recall for your entry rules; adjust thresholds (edge, cp_prob, RSI, EMA alignment) to reach desired trade quality. --- ### 7) Position Sizing & Execution - Position size (conservative Kelly/VaR): - Kelly_cap = 0.25 × Kelly (from optimizer) or ≤ 1.0% equity, whichever is smaller. - VaR sizing: risk_to_SL ≤ risk_budget, where risk_to_SL uses spread‑adjusted SL and conformal lower. - Stops & targets: - TP/SL from optimizer; time stop at horizon if neither is hit. - Optional partial TP at 0.5×TP; move stop to breakeven. - Costs: subtract spread/commission from TP; inflate SL by typical slippage. --- ### 8) Backtest & Walk‑Forward Checks 1) Rolling backtest for chosen forecast method(s) ```bash python cli.py forecast_backtest_run EURUSD --timeframe H1 --horizon 12 \ --steps 50 --spacing 5 --methods "theta fourier_ols" --format json ``` 2) Stress‑test entry thresholds - Sweep edge minima (0.05→0.15), cp_prob caps, regime sets, and confirm Sharpe/win rate stability. --- ### 9) Trade Plans (Examples) Plan A – Breakout with pullback filter - Gates: regime in {trend}, cp_prob<0.6, price>EMA(20)>EMA(50), conformal.lower>pivot. - Targets: TP=0.40%, SL=0.80% (from optimizer); time stop at 12 bars. - Size: min(VaR_budget, 0.25×Kelly) with HAR‑RV sigma. Plan B – Mean‑reversion in range regime (reduced size) - Gates: regime=range & low cp_prob; RSI>70 near R1 or <30 near S1. - Smaller size; tighter SL and 0.20–0.30% TP; MC must show positive edge. --- ### 10) Monitoring & Drift Handling - Update BOCPD and HMM twice per session; stand down on cp spikes. - Refresh HAR‑RV daily (intraday M5 RV aggregation). - Re‑calibrate conformal residuals weekly; re‑grid barrier optimizer monthly. - Track realized vs. forecast errors; trigger re‑training on degradation. --- ## TL;DR – Advanced Flow 1) Filter: regime & cp_prob gates. 2) Calibrate risk with HAR‑RV; set budget. 3) Denoise + quick TI context (EMA/RSI/MACD). 4) Forecast with conformal intervals; gate by bands. 5) Optimize TP/SL via MC; sanity‑check with GBM closed‑form. 6) (Optional) Label history with triple‑barrier; tune thresholds. 7) Execute with VaR/Kelly‑capped sizing, time stop, and costs. 8) Walk‑forward checks; adjust thresholds and monitoring cadence.