rckrdmrd
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>
121 lines
3.6 KiB
SQL
121 lines
3.6 KiB
SQL
-- =====================================================
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-- ML SCHEMA - FEATURE STORE TABLE
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-- =====================================================
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-- Description: Pre-calculated features for ML models
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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.feature_store (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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-- Identificación del feature set
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(10) NOT NULL,
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timestamp TIMESTAMPTZ NOT NULL,
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-- Features técnicos
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technical_features JSONB, -- RSI, MACD, Bollinger, SMA, EMA, etc.
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-- Features de volumen
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volume_features JSONB, -- Volume profiles, OBV, VWAP, etc.
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-- Features de precio
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price_features JSONB, -- Price changes, returns, volatility, etc.
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-- Features de sentimiento (si disponible)
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sentiment_features JSONB, -- Social sentiment, news sentiment, etc.
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-- Features on-chain (para crypto)
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onchain_features JSONB, -- Network metrics, whale activity, etc.
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-- Features derivados
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derived_features JSONB, -- Features calculados de otros features
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-- Metadata
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feature_version VARCHAR(20), -- Versión del cálculo de features
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calculation_timestamp TIMESTAMPTZ NOT NULL DEFAULT NOW(),
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calculation_duration_ms INTEGER,
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created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
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-- Constraints
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CONSTRAINT unique_feature_set UNIQUE(symbol, timeframe, timestamp)
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);
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-- Índices
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CREATE INDEX idx_feature_store_symbol ON ml.feature_store(symbol);
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CREATE INDEX idx_feature_store_symbol_time ON ml.feature_store(symbol, timeframe, timestamp DESC);
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CREATE INDEX idx_feature_store_timestamp ON ml.feature_store(timestamp DESC);
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CREATE INDEX idx_feature_store_version ON ml.feature_store(feature_version);
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-- Particionamiento por fecha (recomendado para alto volumen)
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-- Se puede implementar particionamiento mensual o trimestral
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-- Comentarios
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COMMENT ON TABLE ml.feature_store IS 'Pre-calculated features for ML model inference and training';
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COMMENT ON COLUMN ml.feature_store.symbol IS 'Trading symbol (e.g., BTCUSDT)';
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COMMENT ON COLUMN ml.feature_store.timeframe IS 'Timeframe for features (1m, 5m, 15m, 1h, 4h, 1d)';
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COMMENT ON COLUMN ml.feature_store.timestamp IS 'Timestamp of the candle/bar these features represent';
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COMMENT ON COLUMN ml.feature_store.feature_version IS 'Version of feature calculation logic';
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-- Ejemplos de features JSONB:
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COMMENT ON COLUMN ml.feature_store.technical_features IS
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'Example: {
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"rsi_14": 65.42,
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"rsi_9": 68.21,
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"macd": 0.0234,
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"macd_signal": 0.0189,
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"macd_histogram": 0.0045,
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"bb_upper": 45678.90,
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"bb_middle": 45234.12,
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"bb_lower": 44789.34,
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"sma_20": 45123.45,
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"ema_12": 45234.56,
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"ema_26": 45012.34
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}';
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COMMENT ON COLUMN ml.feature_store.volume_features IS
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'Example: {
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"volume": 1234567.89,
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"volume_sma_20": 1123456.78,
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"volume_ratio": 1.098,
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"obv": 98765432.10,
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"vwap": 45234.12,
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"buy_volume": 678901.23,
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"sell_volume": 555666.66
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}';
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COMMENT ON COLUMN ml.feature_store.price_features IS
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'Example: {
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"open": 45100.00,
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"high": 45500.00,
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"low": 44900.00,
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"close": 45234.12,
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"price_change_1h": 0.0145,
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"price_change_4h": 0.0234,
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"price_change_24h": 0.0567,
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"volatility_1h": 0.0089,
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"atr_14": 234.56
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}';
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COMMENT ON COLUMN ml.feature_store.sentiment_features IS
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'Example: {
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"social_sentiment": 0.72,
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"news_sentiment": 0.65,
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"fear_greed_index": 68,
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"twitter_volume": 12345,
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"reddit_mentions": 678
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}';
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COMMENT ON COLUMN ml.feature_store.onchain_features IS
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'Example: {
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"active_addresses": 123456,
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"transaction_volume": 98765.43,
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"exchange_inflow": 1234.56,
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"exchange_outflow": 2345.67,
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"whale_transactions": 23,
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"network_growth": 0.023
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}';
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