MetaTrader5 MCP Server

""" Forecast method definitions and metadata. """ from typing import Any, Dict, List, Literal # Local fallbacks for typing aliases (avoid import cycle) try: from mtdata.core.server import ForecastMethodLiteral, TimeframeLiteral # type: ignore except Exception: # runtime fallback ForecastMethodLiteral = str TimeframeLiteral = str # Supported forecast methods FORECAST_METHODS = ( "naive", "seasonal_naive", "drift", "theta", "fourier_ols", "ses", "holt", "holt_winters_add", "holt_winters_mul", "arima", "sarima", "mc_gbm", "hmm_mc", "nhits", "nbeatsx", "tft", "patchtst", "sf_autoarima", "sf_theta", "sf_autoets", "sf_seasonalnaive", "mlf_rf", "mlf_lightgbm", "chronos_bolt", "chronos2", "timesfm", "lag_llama", "gt_deepar", "gt_sfeedforward", "gt_prophet", "gt_tft", "gt_wavenet", "gt_deepnpts", "gt_mqf2", "gt_npts", "ensemble", "analog", ) def get_forecast_methods_data() -> Dict[str, Any]: """Get comprehensive data about available forecast methods.""" methods = [] def add(method: str, description: str, params: List[Dict[str, Any]], requires: List[str], supports: Dict[str, bool]) -> None: methods.append({ "method": method, "description": description, "params": params, "requires": requires, "supports": supports }) # Classical methods add("naive", "Last value carried forward", [], [], {"price": True, "return": True, "volatility": True, "ci": False}) add("drift", "Linear trend extension from last observation", [], ["statsmodels"], {"price": True, "return": True, "volatility": True, "ci": False}) add("seasonal_naive", "Last seasonal period value carried forward", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}], ["pandas"], {"price": True, "return": True, "volatility": True, "ci": False}) add("theta", "Theta decomposition with SES and linear drift components", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Smoothing parameter (0.1-1.0, auto if omitted)"}, {"name": "drift", "type": "bool", "description": "Include drift component (default: True)"}], ["statsmodels"], {"price": True, "return": True, "volatility": True, "ci": True}) add("fourier_ols", "Fourier series regression with ordinary least squares", [{"name": "seasonality", "type": "int", "description": "Base seasonal period (auto if omitted)"}, {"name": "terms", "type": "int", "description": "Number of Fourier terms (default: 3)"}, {"name": "trend", "type": "bool", "description": "Include linear trend (default: True)"}], ["statsmodels", "pandas"], {"price": True, "return": True, "volatility": True, "ci": True}) # ETS family add("ses", "Simple exponential smoothing", [{"name": "alpha", "type": "float", "description": "Smoothing parameter (0.1-1.0, auto if omitted)"}, {"name": "damped", "type": "bool", "description": "Apply damping (default: False)"}], ["statsmodels"], {"price": True, "return": True, "volatility": True, "ci": True}) add("holt", "Holt's linear trend method", [{"name": "alpha", "type": "float", "description": "Level smoothing (0.1-1.0, auto if omitted)"}, {"name": "beta", "type": "float", "description": "Trend smoothing (0.1-1.0, auto if omitted)"}, {"name": "damped", "type": "bool", "description": "Apply damping (default: False)"}], ["statsmodels"], {"price": True, "return": True, "volatility": True, "ci": True}) add("holt_winters_add", "Holt-Winters additive seasonality", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Level smoothing (0.1-1.0, auto if omitted)"}, {"name": "beta", "type": "float", "description": "Trend smoothing (0.1-1.0, auto if omitted)"}, {"name": "gamma", "type": "float", "description": "Seasonal smoothing (0.1-1.0, auto if omitted)"}, {"name": "damped", "type": "bool", "description": "Apply damping (default: False)"}], ["statsmodels"], {"price": True, "return": True, "volatility": True, "ci": True}) add("holt_winters_mul", "Holt-Winters multiplicative seasonality", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Level smoothing (0.1-1.0, auto if omitted)"}, {"name": "beta", "type": "float", "description": "Trend smoothing (0.1-1.0, auto if omitted)"}, {"name": "gamma", "type": "float", "description": "Seasonal smoothing (0.1-1.0, auto if omitted)"}, {"name": "damped", "type": "bool", "description": "Apply damping (default: False)"}], ["statsmodels"], {"price": True, "return": False, "volatility": False, "ci": True}) # ARIMA family add("arima", "Autoregressive Integrated Moving Average", [{"name": "order", "type": "tuple", "description": "(p,d,q) order (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Significance level for CI (default: 0.05)"}], ["statsmodels", "pmdarima"], {"price": True, "return": True, "volatility": True, "ci": True}) add("sarima", "Seasonal ARIMA", [{"name": "order", "type": "tuple", "description": "(p,d,q) order (auto if omitted)"}, {"name": "seasonal_order", "type": "tuple", "description": "(P,D,Q,s) order (auto if omitted)"}, {"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Significance level for CI (default: 0.05)"}], ["statsmodels", "pmdarima"], {"price": True, "return": True, "volatility": True, "ci": True}) # Monte Carlo methods add("mc_gbm", "Geometric Brownian Motion Monte Carlo simulation", [{"name": "n_sims", "type": "int", "description": "Number of simulations (default: 1000)"}, {"name": "seed", "type": "int", "description": "Random seed (default: 42)"}, {"name": "mu", "type": "float", "description": "Drift rate (auto-calibrated if omitted)"}, {"name": "sigma", "type": "float", "description": "Volatility (auto-calibrated if omitted)"}], ["numpy", "scipy"], {"price": True, "return": True, "volatility": False, "ci": True}) add("hmm_mc", "Hidden Markov Model Monte Carlo simulation", [{"name": "n_states", "type": "int", "description": "Number of regime states (default: 2)"}, {"name": "n_sims", "type": "int", "description": "Number of simulations (default: 1000)"}, {"name": "seed", "type": "int", "description": "Random seed (default: 42)"}], ["hmmlearn", "numpy"], {"price": True, "return": True, "volatility": False, "ci": True}) # Neural networks (require external dependencies) add("nhits", "N-HiTS neural network", [{"name": "input_size", "type": "int", "description": "Input context length (auto if omitted)"}, {"name": "epochs", "type": "int", "description": "Training epochs (default: 100)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 0.001)"}], ["torch", "pytorch_forecasting"], {"price": True, "return": True, "volatility": True, "ci": True}) add("nbeatsx", "N-BEATS-X neural network with exogenous features", [{"name": "input_size", "type": "int", "description": "Input context length (auto if omitted)"}, {"name": "epochs", "type": "int", "description": "Training epochs (default: 100)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 0.001)"}], ["torch", "pytorch_forecasting"], {"price": True, "return": True, "volatility": True, "ci": True}) add("tft", "Temporal Fusion Transformer", [{"name": "input_size", "type": "int", "description": "Input context length (auto if omitted)"}, {"name": "epochs", "type": "int", "description": "Training epochs (default: 50)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 0.001)"}], ["torch", "pytorch_forecasting"], {"price": True, "return": True, "volatility": True, "ci": True}) add("patchtst", "PatchTST transformer", [{"name": "input_size", "type": "int", "description": "Input context length (auto if omitted)"}, {"name": "epochs", "type": "int", "description": "Training epochs (default: 100)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 0.001)"}], ["torch", "patchtst"], {"price": True, "return": True, "volatility": True, "ci": True}) # StatsForecast methods add("sf_autoarima", "StatsForecast AutoARIMA", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Significance level for CI (default: 0.05)"}], ["statsforecast"], {"price": True, "return": True, "volatility": True, "ci": True}) add("sf_theta", "StatsForecast Theta method", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "decomposition_type", "type": "str", "description": "Multiplicative or additive (auto if omitted)"}], ["statsforecast"], {"price": True, "return": True, "volatility": True, "ci": True}) add("sf_autoets", "StatsForecast AutoETS", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}, {"name": "alpha", "type": "float", "description": "Significance level for CI (default: 0.05)"}], ["statsforecast"], {"price": True, "return": True, "volatility": True, "ci": True}) add("sf_seasonalnaive", "StatsForecast Seasonal Naive", [{"name": "seasonality", "type": "int", "description": "Seasonal period (auto if omitted)"}], ["statsforecast"], {"price": True, "return": True, "volatility": True, "ci": True}) # Machine Learning methods add("mlf_rf", "MLForecast Random Forest", [{"name": "n_estimators", "type": "int", "description": "Number of trees (default: 100)"}, {"name": "max_depth", "type": "int", "description": "Maximum depth (default: None)"}, {"name": "lags", "type": "list", "description": "Lag features to use (auto if omitted)"}], ["mlforecast", "scikit-learn"], {"price": True, "return": True, "volatility": True, "ci": True}) add("mlf_lightgbm", "MLForecast LightGBM", [{"name": "num_leaves", "type": "int", "description": "Number of leaves (default: 31)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 0.1)"}, {"name": "lags", "type": "list", "description": "Lag features to use (auto if omitted)"}], ["mlforecast", "lightgbm"], {"price": True, "return": True, "volatility": True, "ci": True}) # Pre-trained models add("chronos_bolt", "Chronos-2 foundation model (alias: chronos2). Successor to Chronos-Bolt; upstream supports cross-learning, multivariate, and covariates—current adapter uses univariate target only.", [{"name": "model_name", "type": "str", "description": "Hugging Face model id (default: amazon/chronos-2)"}, {"name": "context_length", "type": "int", "description": "Context window length (auto if omitted)"}, {"name": "quantiles", "type": "list", "description": "Quantile levels to return (default: [0.5])"}, {"name": "device_map", "type": "str", "description": "Device map for loading (default: auto)"}], ["chronos-forecasting>=2.0.0", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("chronos2", "Chronos-2 foundation model (preferred name; same implementation as chronos_bolt). Upstream supports cross-learning, multivariate, and covariates—current adapter uses univariate target only.", [{"name": "model_name", "type": "str", "description": "Hugging Face model id (default: amazon/chronos-2)"}, {"name": "context_length", "type": "int", "description": "Context window length (auto if omitted)"}, {"name": "quantiles", "type": "list", "description": "Quantile levels to return (default: [0.5])"}, {"name": "device_map", "type": "str", "description": "Device map for loading (default: auto)"}], ["chronos-forecasting>=2.0.0", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("timesfm", "Google TimesFM pre-trained time series foundation model", [{"name": "device", "type": "str", "description": "Compute device (cpu/cuda, default: auto)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 16)"}], ["timesfm", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("lag_llama", "Lag-Llama pre-trained time series model", [{"name": "device", "type": "str", "description": "Compute device (cpu/cuda, default: auto)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 1)"}], ["lag-llama", "gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) # GluonTS Torch quick-train models add("gt_deepar", "GluonTS DeepAR (quick train on series)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(64,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "freq", "type": "str", "description": "Pandas frequency string (auto from timeframe)"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_sfeedforward", "GluonTS SimpleFeedForward (quick train)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(64,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "freq", "type": "str", "description": "Pandas frequency string (auto from timeframe)"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_prophet", "GluonTS Prophet wrapper", [{"name": "freq", "type": "str", "description": "Pandas frequency string (auto from timeframe)"}, {"name": "prophet_params", "type": "dict", "description": "Passed to ProphetPredictor (growth, seasonality_mode, ... )"}], ["gluonts", "prophet"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_tft", "GluonTS Temporal Fusion Transformer (quick train)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(128,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "hidden_size", "type": "int", "description": "Model width (default: 64)"}, {"name": "dropout", "type": "float", "description": "Dropout (default: 0.1)"}, {"name": "freq", "type": "str", "description": "Pandas frequency (auto from timeframe)"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_wavenet", "GluonTS WaveNet (quick train)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(128,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "dilation_depth", "type": "int", "description": "Dilation depth (default: 5)"}, {"name": "num_blocks", "type": "int", "description": "WaveNet blocks (default: 1)"}, {"name": "freq", "type": "str", "description": "Pandas frequency (auto from timeframe)"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_deepnpts", "GluonTS DeepNPTS (quick train)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(128,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "freq", "type": "str", "description": "Pandas frequency (auto from timeframe)"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_mqf2", "GluonTS MQF2 (quick train, quantile-focused)", [{"name": "context_length", "type": "int", "description": "Input window length (default: min(128,n))"}, {"name": "train_epochs", "type": "int", "description": "Training epochs (default: 5)"}, {"name": "batch_size", "type": "int", "description": "Batch size (default: 32)"}, {"name": "learning_rate", "type": "float", "description": "Learning rate (default: 1e-3)"}, {"name": "freq", "type": "str", "description": "Pandas frequency (auto from timeframe)"}, {"name": "quantiles", "type": "list", "description": "Quantiles to return (e.g., [0.05,0.5,0.95])"}], ["gluonts", "torch"], {"price": True, "return": True, "volatility": True, "ci": True}) add("gt_npts", "GluonTS NPTS (non-parametric, fast)", [{"name": "freq", "type": "str", "description": "Pandas frequency string (auto from timeframe)"}, {"name": "season_length", "type": "int", "description": "Season length (default: 1)"}, {"name": "kernel", "type": "str", "description": "Kernel: parzen|mean|median (default: parzen)"}, {"name": "window_size", "type": "int", "description": "Window size (default: min(256,n))"}], ["gluonts"], {"price": True, "return": True, "volatility": True, "ci": True}) # Ensemble methods add("ensemble", "Adaptive ensemble with averaging, Bayesian model averaging, or stacking", [{"name": "methods", "type": "list", "description": "Methods to ensemble (default: naive,theta,fourier_ols)"}, {"name": "mode", "type": "str", "description": "average|bma|stacking (default: average)"}, {"name": "weights", "type": "list", "description": "Manual weights when mode=average"}, {"name": "cv_points", "type": "int", "description": "Walk-forward anchors for weighting (default: 2*len(methods))"}, {"name": "min_train_size", "type": "int", "description": "Minimum history per CV anchor (default: max(30, horizon*3))"}, {"name": "method_params", "type": "dict", "description": "Per-method parameter overrides"}, {"name": "expose_components", "type": "bool", "description": "Include component forecasts in response (default: True)"}], [], {"price": True, "return": True, "volatility": True, "ci": False}) # Analog / Nearest Neighbor add("analog", "Nearest-neighbor search based on historical patterns", [{"name": "window_size", "type": "int", "description": "Length of pattern to match (default: 64)"}, {"name": "search_depth", "type": "int", "description": "Bars back to search (default: 5000)"}, {"name": "top_k", "type": "int", "description": "Number of analogs (default: 20)"}, {"name": "metric", "type": "str", "description": "Similarity metric: euclidean|cosine|correlation (default: euclidean)"}, {"name": "scale", "type": "str", "description": "zscore|minmax|none (default: zscore)"}, {"name": "refine_metric", "type": "str", "description": "dtw|softdtw|affine|ncc|none (default: dtw)"}, {"name": "search_engine", "type": "str", "description": "ckdtree|hnsw|matrix_profile|mass (default: ckdtree)"}, {"name": "secondary_timeframes", "type": "str|list", "description": "List of timeframes to ensemble (e.g. 'D1,H4')"}], ["scipy", "numpy"], {"price": True, "return": False, "volatility": False, "ci": True}) return { "methods": methods, "total": len(methods), "categories": { "classical": ["naive", "drift", "seasonal_naive", "theta", "fourier_ols"], "ets": ["ses", "holt", "holt_winters_add", "holt_winters_mul"], "arima": ["arima", "sarima"], "monte_carlo": ["mc_gbm", "hmm_mc"], "neural": ["nhits", "nbeatsx", "tft", "patchtst"], "statsforecast": ["sf_autoarima", "sf_theta", "sf_autoets", "sf_seasonalnaive"], "machine_learning": ["mlf_rf", "mlf_lightgbm"], "pretrained": ["chronos_bolt", "chronos2", "timesfm", "lag_llama"], "ensemble": ["ensemble"] } } def get_method_category(method: str) -> str: """Get the category of a forecast method.""" categories = { "naive": "classical", "drift": "classical", "seasonal_naive": "classical", "theta": "classical", "fourier_ols": "classical", "ses": "ets", "holt": "ets", "holt_winters_add": "ets", "holt_winters_mul": "ets", "arima": "arima", "sarima": "arima", "mc_gbm": "monte_carlo", "hmm_mc": "monte_carlo", "nhits": "neural", "nbeatsx": "neural", "tft": "neural", "patchtst": "neural", "sf_autoarima": "statsforecast", "sf_theta": "statsforecast", "sf_autoets": "statsforecast", "sf_seasonalnaive": "statsforecast", "mlf_rf": "machine_learning", "mlf_lightgbm": "machine_learning", "chronos_bolt": "pretrained", "chronos2": "pretrained", "timesfm": "pretrained", "lag_llama": "pretrained", "ensemble": "ensemble", "analog": "analog" } return categories.get(method, "unknown") def get_method_requirements(method: str) -> List[str]: """Get the list of required packages for a method.""" method_data = get_forecast_methods_data() for m in method_data["methods"]: if m["method"] == method: return m["requires"] return [] def get_method_supports(method: str) -> Dict[str, bool]: """Get the supported data types and features for a method.""" method_data = get_forecast_methods_data() for m in method_data["methods"]: if m["method"] == method: return m["supports"] return {"price": False, "return": False, "volatility": False, "ci": False} def validate_method_params(method: str, params: Dict[str, Any]) -> List[str]: """Validate method parameters and return list of errors.""" errors = [] # Get method definition method_data = get_forecast_methods_data() method_def = None for m in method_data["methods"]: if m["method"] == method: method_def = m break if not method_def: errors.append(f"Unknown method: {method}") return errors # Check parameter types for param_def in method_def["params"]: param_name = param_def["name"] param_type = param_def["type"] if param_name in params: param_value = params[param_name] # Type validation if param_type == "int": try: int(param_value) except (ValueError, TypeError): errors.append(f"Parameter '{param_name}' should be an integer") elif param_type == "float": try: float(param_value) except (ValueError, TypeError): errors.append(f"Parameter '{param_name}' should be a float") elif param_type == "bool": if not isinstance(param_value, bool): errors.append(f"Parameter '{param_name}' should be a boolean") elif param_type == "tuple": if not isinstance(param_value, (list, tuple)): errors.append(f"Parameter '{param_name}' should be a tuple or list") elif len(param_value) != 3: # Assuming (p,d,q) order errors.append(f"Parameter '{param_name}' should have 3 elements") return errors