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45e77e9a9c
DDL schemas for Trading Platform: - User management - Authentication - Payments - Education - ML predictions - Trading data Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
95 lines
3.5 KiB
SQL
95 lines
3.5 KiB
SQL
-- =====================================================
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-- ML SCHEMA - PREDICTIONS TABLE
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-- =====================================================
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-- Description: ML model predictions and signals
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-- Schema: ml
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-- Author: Database Agent
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-- Date: 2025-12-06
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-- =====================================================
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CREATE TABLE ml.predictions (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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-- Modelo y versión
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model_id UUID NOT NULL REFERENCES ml.models(id) ON DELETE CASCADE,
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model_version_id UUID NOT NULL REFERENCES ml.model_versions(id) ON DELETE CASCADE,
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-- Símbolo y timeframe
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(10) NOT NULL,
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-- Tipo de predicción
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prediction_type ml.prediction_type NOT NULL,
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-- Resultado de predicción
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prediction_result ml.prediction_result,
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prediction_value DECIMAL(20,8), -- Para predicciones numéricas
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-- Confianza
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confidence_score DECIMAL(5,4) NOT NULL CHECK (confidence_score >= 0 AND confidence_score <= 1),
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-- Input features utilizados
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input_features JSONB NOT NULL,
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-- Output completo del modelo
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model_output JSONB, -- Raw output del modelo
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-- Contexto de mercado al momento de predicción
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market_price DECIMAL(20,8),
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market_timestamp TIMESTAMPTZ NOT NULL,
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-- Horizonte temporal
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prediction_horizon VARCHAR(20), -- 1h, 4h, 1d, 1w
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valid_until TIMESTAMPTZ,
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-- Metadata
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prediction_metadata JSONB,
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-- Procesamiento
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inference_time_ms INTEGER, -- Tiempo de inferencia en milisegundos
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created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
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);
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-- Índices
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CREATE INDEX idx_predictions_model ON ml.predictions(model_id);
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CREATE INDEX idx_predictions_version ON ml.predictions(model_version_id);
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CREATE INDEX idx_predictions_symbol ON ml.predictions(symbol);
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CREATE INDEX idx_predictions_symbol_time ON ml.predictions(symbol, market_timestamp DESC);
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CREATE INDEX idx_predictions_type ON ml.predictions(prediction_type);
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CREATE INDEX idx_predictions_created ON ml.predictions(created_at DESC);
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-- Index for predictions with validity period (without time-based predicate for immutability)
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CREATE INDEX idx_predictions_valid ON ml.predictions(valid_until)
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WHERE valid_until IS NOT NULL;
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-- Particionamiento por fecha (opcional, para alto volumen)
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-- CREATE INDEX idx_predictions_timestamp ON ml.predictions(market_timestamp DESC);
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-- Comentarios
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COMMENT ON TABLE ml.predictions IS 'ML model predictions and trading signals';
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COMMENT ON COLUMN ml.predictions.prediction_type IS 'Type of prediction being made';
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COMMENT ON COLUMN ml.predictions.prediction_result IS 'Categorical result (buy/sell/hold/up/down/neutral)';
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COMMENT ON COLUMN ml.predictions.prediction_value IS 'Numeric prediction value (e.g., target price, probability)';
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COMMENT ON COLUMN ml.predictions.confidence_score IS 'Model confidence in prediction (0.0 to 1.0)';
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COMMENT ON COLUMN ml.predictions.input_features IS 'Feature values used for this prediction';
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COMMENT ON COLUMN ml.predictions.prediction_horizon IS 'Time horizon for prediction validity';
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COMMENT ON COLUMN ml.predictions.inference_time_ms IS 'Model inference latency in milliseconds';
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-- Ejemplo de input_features JSONB:
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COMMENT ON COLUMN ml.predictions.input_features IS
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'Example: {
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"rsi_14": 65.42,
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"macd_signal": 0.0234,
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"volume_sma_20": 1234567.89,
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"price_change_1h": 0.0145,
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"sentiment_score": 0.72
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}';
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-- Ejemplo de model_output JSONB:
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COMMENT ON COLUMN ml.predictions.model_output IS
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'Example: {
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"probabilities": {"buy": 0.72, "sell": 0.15, "hold": 0.13},
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"raw_score": 0.5823,
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"feature_contributions": {...}
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}';
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