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Install the MetaTrader5 terminal.\n2. Install Python dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Configuration\n\nYou can configure the server by creating a `.env` file in the project root. This is optional, as the server can connect to an already running and logged-in MetaTrader5 terminal.\n\nTo pre-configure credentials, create a `.env` file and add the following:\n\n```\nMT5_LOGIN=\nMT5_PASSWORD=\nMT5_SERVER=\n```\n\n**Timezone Configuration:**\n- If your MT5 server time is not UTC, set `MT5_TIME_OFFSET_MINUTES` in the `.env` file to the offset in minutes (can be negative).\n- Example: For UTC+2, use `MT5_TIME_OFFSET_MINUTES=120`. For UTC-4, use `MT5_TIME_OFFSET_MINUTES=-240`.\n- All timestamps are normalized to UTC using this offset.\n\n## Usage\n\n### Starting the MCP Server\n\n```bash\npython server.py\n```\n\nThe server exposes tools that can be called programmatically or via the command-line interface (`cli.py`).\n\n### Available Tools (CLI)\n\nThe following tools are available via `python cli.py `:\n\n#### Market Data\n- `symbols_list [--search-term TERM] [--limit N]` - Smart search for trading symbols.\n- `symbols_list_groups [--search-term TERM] [--limit N]` - Get available symbol groups.\n- `symbols_describe ` - Get detailed symbol information.\n- `data_fetch_candles [--timeframe TF] [--limit N]` - Get historical OHLCV data.\n- `data_fetch_ticks [--limit N]` - Get tick data.\n- `market_depth_fetch ` - Get market depth (DOM).\n- `indicators_list` - List available technical indicators. (Use `list_capabilities --sections indicators --include-details true` for JSON details.)\n - CSV: grouped rows (category shown once per group):\n category,name\n Momentum,RSI\n ,MACD\n ,Stoch\n - JSON: grouped by category `{ \"categories\": { \"Momentum\": [\"RSI\", ...], ... } }`\n- `indicators_describe ` - Get detailed information for a specific indicator.\n- `denoise_list_methods` - List available denoising methods and their parameters.\n- `patterns_detect_candlesticks [--timeframe TF] [--limit N]` - Detect candlestick patterns.\n- `pivot_compute_points [--timeframe TF]` - Compute pivot point levels across all supported methods.\n- `forecast_generate [--timeframe TF] [--method METHOD] [--horizon N] ...` - Generate price forecasts.\n- `forecast_volatility_estimate [--timeframe TF] [--horizon N] [--method METHOD] [--proxy PROXY]` - Forecast volatility using direct estimators, GARCH, or general forecasters on a proxy.\n- `report_generate [--horizon N] [--template basic|advanced|scalping|intraday|swing|position]` - One-stop consolidated report rendered as Markdown (context, pivots, vol, backtest->best forecast, MC barriers; advanced adds regimes, HAR-RV, conformal). Templates infer timeframes.\n - Default horizons per template: scalping=8 bars, intraday=12, swing=24, position=30, basic/advanced=12. Override with `--horizon` or `--params \"horizon=...\"` if needed.\n \n- `list_capabilities [--sections ...] [--include-details true|false]` - Consolidated features: frameworks, forecast/volatility methods, denoise, indicators, dimred, and pattern_search backends.\n\n*(Note: Programmatic access uses function names like `get_symbols`, `get_rates`, etc.)*\n\n### Example Usage\n\n#### Sample Trade Guides\n\n- Beginner flow: docs/SAMPLE-TRADE.md\n- Advanced flow (regimes, HAR-RV, conformal, MC barriers, risk controls): docs/SAMPLE-TRADE-ADVANCED.md\n\n#### One-Stop Consolidated Report\n\n```bash\n# Basic report (context, pivots, EWMA vol, backtest->best forecast, MC barrier grid)\npython cli.py report_generate EURUSD --template basic\n\n# Advanced report (adds regime summaries, HAR-RV vol, conformal intervals)\npython cli.py report_generate EURUSD --template advanced\n\n# Style-specific templates\npython cli.py report_generate EURUSD --template scalping\npython cli.py report_generate EURUSD --template intraday\npython cli.py report_generate EURUSD --template swing\npython cli.py report_generate EURUSD --template position\n\n# Optional fine-tuning via params (grid and backtest sizing)\npython cli.py report_generate EURUSD --horizon 12 --template basic \\\n --params \"backtest_steps=25 backtest_spacing=10 tp_min=0.2 tp_max=1.0 tp_steps=5 sl_min=0.2 sl_max=1.0 sl_steps=5 top_k=5\"\n```\n\n#### Fetching Data\n\n```bash\n# Get all visible symbols\npython cli.py symbols_list\n\n# Smart search for symbols containing \"EUR\"\npython cli.py symbols_list --search-term EUR --limit 20\n\n# Get EUR/USD hourly data\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 100\n\n# Get tick data\npython cli.py data_fetch_ticks EURUSD --limit 50\npython cli.py data_fetch_ticks EURUSD --start \"yesterday 14:00\" --end \"yesterday 15:00\" --limit 200\n```\n\n#### Data Simplification\n\nThe `data_fetch_candles` and `data_fetch_ticks` commands support a powerful `--simplify` and `--simplify-params` option to downsample or transform data.\n\n**Modes & Methods:**\n\n**1. SELECT Mode (Default)**\n- **`lttb`** (Largest Triangle Three Buckets): General-purpose downsampling preserving chart shape\n- **`rdp`** (Ramer-Douglas-Peucker): Error-tolerance based simplification\n- **`pla`** (Piecewise Linear Approximation): Linear segment approximation\n- **`apca`** (Adaptive Piecewise Constant Approximation): Constant segment approximation\n\n**2. RESAMPLE Mode**\n- Buckets data into fixed time intervals\n- Parameters: `bucket_seconds` (time window in seconds)\n\n**3. ENCODE Mode**\n- **`envelope`**: High/Low bands with Open/Close position encoding\n- **`delta`**: Integer or character-encoded deltas of O/H/L/C vs previous close\n- Parameters: `bits` (precision), `as_chars` (character output), `alphabet` (custom symbols), `scale` (delta tick size)\n\n**4. SEGMENT Mode (ZigZag)**\n- **`zigzag`** (alias: `zz`): Detects price turning points and trend changes\n- Parameters: `threshold_pct` (minimum % change), `value_col` (price column to analyze)\n- Preserves all original columns at pivot rows and adds: `value`, `direction`, `change_pct`.\n\n**5. SYMBOLIC Mode**\n- **`sax`** (Symbolic Aggregate approXimation): Converts time series to symbolic patterns\n- Parameters: `paa` (segments), `alphabet` (symbol set), `znorm` (z-normalization)\n- Preserves all requested columns via per-segment aggregation (mean for numeric columns). Time is represented by `start_time` and `end_time`.\n\n**6. APPROXIMATE Mode**\n- Aggregates data into segments based on selected points\n\n### Simplify Method Comparison\n\n| Method | Best For | Output Size | Key Parameters |\n|--------|----------|-------------|-----------------|\n| `lttb` | General downsampling | User-controlled | `points`, `ratio` |\n| `rdp` | Shape preservation | Varies | `epsilon` (tolerance) |\n| `zigzag` | Swing/trend analysis | Varies | `threshold_pct` |\n| `sax` | Pattern recognition | Fixed segments | `paa`, `alphabet` |\n| `envelope`/`delta` | Compact storage | Fixed | `bits`, `as_chars`, `scale` |\n| `resample` | Regular intervals | Time-based | `bucket_seconds` |\n\nNotes:\n- Encode preserves all requested columns (e.g., indicators, volumes); it replaces OHLC with envelope positions or deltas.\n- When a transform changes columns, the response includes the exact output headers in metadata.\n\n**CLI Examples:**\n\n```bash\n# Select 200 points using LTTB algorithm\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 2000 --simplify lttb --simplify-params points=200 --format json\n\n# Use ratio instead of fixed points\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 1000 --simplify lttb --simplify-params ratio=0.2 --format json\n\n# RDP with error tolerance\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 1000 --simplify rdp --simplify-params epsilon=0.001 --format json\n\n# ZigZag trend analysis - sensitive (0.2% threshold)\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 500 --simplify segment --simplify-params \"algo=zigzag,threshold_pct=0.2\" --format json\n\n# ZigZag swing analysis - moderate (0.6% threshold)\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 500 --simplify segment --simplify-params \"algo=zigzag,threshold_pct=0.6\" --format json\n\n# Resample into 6-hour buckets\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 2000 --simplify resample --simplify-params bucket_seconds=21600 --format json\n\n# Envelope encoding with numeric positions\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 200 --simplify encode --simplify-params \"schema=envelope,bits=8\" --format json\n\n# Envelope encoding with character output (compact UTF-8)\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 200 --simplify encode --simplify-params \"schema=envelope,as_chars=true,bits=4\" --format json\n\n# Delta encoding (scaled integer or base-N char deltas)\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 300 --simplify encode --simplify-params \"schema=delta,scale=1e-5,as_chars=true,zero_char=.\" --format json\n\n# Symbolic representation (SAX)\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 500 --simplify symbolic --simplify-params \"schema=sax,paa=24,znorm=true\" --format json\n\n# Approximate mode with aggregation\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 2000 --simplify approximate --simplify-params points=150 --format json\n\n# ZigZag on different price columns\npython cli.py data_fetch_candles EURUSD --timeframe H1 --limit 500 --simplify segment --simplify-params \"algo=zigzag,threshold_pct=0.8,value_col=high\" --format json\n```\n\n### Forecasting\n\nGenerate point forecasts for the next `horizon` bars. The server supports lightweight classical models, Monte Carlo/HMM simulation, and optional integrations with modern forecasting frameworks.\n\n- Discover methods: `python cli.py list_capabilities --sections forecast`\n- Run forecast: `python cli.py forecast_generate --timeframe --method --horizon [--params JSON]`\n- Rolling backtest: `python cli.py forecast_backtest_run --timeframe --horizon [--steps S --spacing K --methods ...]`\n- Volatility forecasts: use `forecast_volatility_estimate` with methods like `ewma`, `parkinson`, `har_rv`, `garch`, or general `arima`/`ets` with a `--proxy` (e.g., `log_r2`).\n\nClassical example\n```bash\npython cli.py forecast_generate EURUSD --timeframe H1 --method theta --horizon 12 --format json\n```\n\nHAR-RV volatility (daily RV from M5 returns)\n```bash\npython cli.py forecast_volatility_estimate EURUSD --timeframe H1 --horizon 12 \\\n --method har_rv --params \"rv_timeframe=M5,days=150,window_w=5,window_m=22\" --format json\n```\n\nMonte Carlo forecasts (distribution + bands)\n```bash\n# GBM Monte Carlo\npython cli.py forecast_generate EURUSD --timeframe H1 --method mc_gbm --horizon 12 --params \"n_sims=2000 seed=7\" --format json\n# Regime-aware HMM Monte Carlo\npython cli.py forecast_generate EURUSD --timeframe H1 --method hmm_mc --horizon 12 --params \"n_states=3 n_sims=3000 seed=7\" --format json\n```\n\nBarrier analytics (TP/SL odds from MC paths)\n```bash\n# Probability of hitting TP before SL within 12 bars (percent barriers)\npython cli.py forecast_barrier_hit_probabilities --symbol EURUSD --timeframe H1 --horizon 12 \\\n --method hmm_mc --tp_pct 0.5 --sl_pct 0.3 --params \"n_sims=5000 seed=7\" --format json\n\n# Optimize TP/SL grid to maximize edge/Kelly/EV (percent mode)\npython cli.py forecast_barrier_optimize --symbol EURUSD --timeframe H1 --horizon 12 \\\n --method hmm_mc --mode pct --tp_min 0.2 --tp_max 1.0 --tp_steps 5 --sl_min 0.2 --sl_max 1.0 --sl_steps 5 \\\n --params \"n_sims=5000 seed=7\" --format json\n```\n\nFor advanced usage, pattern-based signals, Monte Carlo/HMM details, and barrier analytics, see `docs/FORECAST.md`.\n\n### Regime & Change-Points\n\nDetect significant structural changes and market regimes to adapt strategies:\n\n```bash\n# Bayesian Online Change-Point Detection (BOCPD) on log-returns\npython cli.py regime_detect EURUSD --timeframe H1 --limit 1000 --method bocpd --threshold 0.6 --format json\n\n# HMM-lite regime labeling (Gaussian mixture over returns)\npython cli.py regime_detect EURUSD --timeframe H1 --limit 1000 --method hmm --params \"n_states=3\" --format json\n\n# Markov-Switching AR (statsmodels, if available)\npython cli.py regime_detect EURUSD --timeframe H1 --limit 1200 --method ms_ar --params \"k_regimes=2 order=1\" --format json\n```\n\nUse `cp_prob` and `change_points` from BOCPD to reset models/sizes after structural breaks; use `state`/`state_probabilities` from HMM/MS-AR to switch playbooks (trend vs. range) and adjust risk.\n\n### Conformal Prediction (Valid Intervals)\n\nBuild statistically valid prediction intervals around any base method:\n\n```bash\npython cli.py forecast_conformal_intervals EURUSD --timeframe H1 --method theta --horizon 12 \\\n --steps 25 --spacing 10 --alpha 0.1 --format json\n```\n\nThis runs a rolling backtest to calibrate per-step residual quantiles and then returns point forecast + conformal bands.\n\n### Triple-Barrier Labeling\n\nCreate labels (+1 TP first, -1 SL first, 0 neither) for supervised learning and signal analysis:\n\n```bash\npython cli.py labels_triple_barrier EURUSD --timeframe H1 --limit 1500 --horizon 12 \\\n --tp_pct 0.5 --sl_pct 0.3 --label-on high_low --format json\n```\n\nUse these labels to train meta-models or to evaluate rule performance across regimes.\n\n### Closed-Form Barrier (GBM)\n\nCompute single-barrier hit probability using GBM formulas (fast sanity check vs. MC):\n\n```bash\npython cli.py forecast_barrier_closed_form EURUSD --timeframe H1 --horizon 12 --direction up --barrier 1.1000 --format json\n```\n\n#### Dimensionality Reduction (Pattern Search)\n\n- Discover reducers: `python cli.py list_capabilities --sections dimred`\n- Use PCA quickly: `--pca-components 8`\n- Flexible: `--dimred-method pca|kpca|umap|isomap|diffusion|dreams_cne|dreams_cne_fast --dimred-params \"key=value,...\"`\n- Notes:\n - Prefer reducers with transform support for online queries (e.g., PCA, KPCA, UMAP, parametric DREAMS-CNE).\n - t-SNE/Laplacian embeddings cannot transform new queries; avoid for pattern_search.\n - DREAMS-CNE requires installation from source; `dreams_cne_fast` uses lighter defaults for speed.\n\n#### Optional Framework Integrations\n\nYou can extend forecasting with additional frameworks. Install as needed; methods will appear under `forecast_methods` in `list_capabilities` once available.\n\n- StatsForecast (classical, fast): `pip install statsforecast`\n - Methods: `sf_autoarima`, `sf_theta`, `sf_autoets`, `sf_seasonalnaive`\n- MLForecast (tree/GBMs over lags): `pip install mlforecast scikit-learn` (and `lightgbm` for LGBM)\n - Methods: `mlf_rf`, `mlf_lightgbm`\n- NeuralForecast (deep learning): `pip install neuralforecast[torch]`\n - Methods: `nhits`, `nbeatsx`, `tft`, `patchtst`\n- Foundation models (Transformers/Chronos/TimesFM): `pip install transformers torch accelerate` (plus `chronos-forecasting` or `timesfm` if desired)\n - Methods: `chronos_bolt`, `timesfm`, `lag_llama`\n\nSee detailed examples and parameters in `docs/FORECAST.md` under Framework Integrations.\n\n\n### Denoising\n\nApply smoothing algorithms to data columns.\n\n- **Methods:** `ema`, `sma`, `median`, `lowpass_fft`, `wavelet`, `emd`, `eemd`, `ceemdan`.\n- **Discover methods:** `python cli.py denoise_list_methods`\n\n```bash\n# Denoise OHLCV with an EMA before simplifying\npython cli.py data_fetch_candles EURUSD --timeframe H1 --denoise ema --simplify lttb --simplify-params points=50 --limit 1000 --format json\n\n# Denoise the close and an RSI indicator after they are calculated\npython cli.py data_fetch_candles EURUSD --timeframe H1 --indicators \"rsi(14)\" --denoise ema --denoise-params \"columns=close,RSI_14,when=post_ti\" --format json\n```\n\n## Intelligent Symbol Search\n\nThe `symbols_list` command uses a smart 3-tier search strategy:\n1. **Group name match** (e.g., `Majors`, `Forex`)\n2. **Symbol name match** (e.g., `EUR` → EURUSD, JPYEUR, ...)\n3. **Description match** (symbol or group path)\n\n## Date Inputs\n\nThe `start` and `end` parameters for `data_fetch_candles` use `dateparser` for flexible date/time parsing.\n- **Natural language:** `yesterday 14:00`, `2 days ago`\n- **Common formats:** `2025-08-29`, `2025/08/29 14:30`\n\n## Requirements\n\n- Windows OS (MetaTrader5 requirement)\n- MetaTrader5 terminal installed and running\n- Python 3.8+\n- Active internet connection\n\nOptional dependencies (uncomment in `requirements.txt`):\n- Forecast frameworks: `statsforecast`, `mlforecast`, `lightgbm`, `neuralforecast`\n- Foundation models: `transformers`, `accelerate`, `torch`, `chronos-forecasting`, `timesfm`\n- Pattern search backends: `hnswlib`, `tslearn`, `dtaidistance`\n- Dimensionality reduction: `scikit-learn`, `umap-learn`, `pydiffmap`, `pykeops`, and DREAMS-CNE from source\n\n## Error Handling\n\nAll functions return a JSON object with a `success` flag. If `success` is `false`, an `error` field will contain the error message.\n\n## Security Notes\n\n- For unattended use, store credentials securely in a `.env` file.\n- Do not commit `.env` files to version control.\n- Use demo accounts for testing.\n\n## License\n\nMIT License\n\n\n\n\n\n","English","tag","en-US",{"_41":42,"_13":43,"_50":32,"_51":32,"_16":52,"_53":54,"_33":34,"_79":80,"_87":-5,"_88":89,"_127":128,"_132":-5,"_61":112,"_133":116,"_64":134,"_135":136},"addedAt","2025-08-31T04:01:13.835109Z",[44,45,46,47,48,49],"category:finance","category:data-platforms","environment:local","hosting:local-only","language:python","license_grade:f","descriptionMarkdown","descriptionPlainText",10975,"integrations",[55,66,72],{"_56":57,"_31":63,"_64":65},"brand",{"_58":59,"_33":60,"_61":62},"logoSvgPath","M24 0v24H0V0h24ZM10.933 15.89H6.84v5.52h4.198v-.93H7.955v-1.503h2.77v-.93h-2.77v-1.224h2.978v-.934Zm2.146 0h-1.084v5.52h1.035v-3.6l2.226 3.6h1.118v-5.52h-1.036v3.686l-2.259-3.687Zm5.117 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