MaverickMCP

"""Remove proprietary terminology from columns Revision ID: 011_remove_proprietary_terms Revises: 010_self_contained_schema Create Date: 2025-01-10 This migration removes proprietary terminology from database columns: - rs_rating → momentum_score (more descriptive of what it measures) - vcp_status → consolidation_status (generic pattern description) Updates all related indexes and handles both PostgreSQL and SQLite databases. """ import sqlalchemy as sa from sqlalchemy.dialects import postgresql from alembic import op # revision identifiers revision = "011_remove_proprietary_terms" down_revision = "010_self_contained_schema" branch_labels = None depends_on = None def upgrade(): """Remove proprietary terminology from columns.""" # Check if we're using PostgreSQL or SQLite bind = op.get_bind() dialect_name = bind.dialect.name if dialect_name == "postgresql": print("🗃️ PostgreSQL: Renaming columns and indexes...") # 1. Rename columns in mcp_maverick_stocks print(" 📊 Updating mcp_maverick_stocks...") op.alter_column( "mcp_maverick_stocks", "rs_rating", new_column_name="momentum_score" ) op.alter_column( "mcp_maverick_stocks", "vcp_status", new_column_name="consolidation_status" ) # 2. Rename columns in mcp_maverick_bear_stocks print(" 🐻 Updating mcp_maverick_bear_stocks...") op.alter_column( "mcp_maverick_bear_stocks", "rs_rating", new_column_name="momentum_score" ) op.alter_column( "mcp_maverick_bear_stocks", "vcp_status", new_column_name="consolidation_status", ) # 3. Rename columns in mcp_supply_demand_breakouts print(" 📈 Updating mcp_supply_demand_breakouts...") op.alter_column( "mcp_supply_demand_breakouts", "rs_rating", new_column_name="momentum_score" ) op.alter_column( "mcp_supply_demand_breakouts", "vcp_status", new_column_name="consolidation_status", ) # 4. Rename indexes to use new column names print(" 🔍 Updating indexes...") op.execute( "ALTER INDEX IF EXISTS mcp_maverick_stocks_rs_rating_idx RENAME TO mcp_maverick_stocks_momentum_score_idx" ) op.execute( "ALTER INDEX IF EXISTS mcp_maverick_bear_stocks_rs_rating_idx RENAME TO mcp_maverick_bear_stocks_momentum_score_idx" ) op.execute( "ALTER INDEX IF EXISTS mcp_supply_demand_breakouts_rs_rating_idx RENAME TO mcp_supply_demand_breakouts_momentum_score_idx" ) # 5. Update any legacy indexes that might still exist op.execute( "ALTER INDEX IF EXISTS idx_stocks_supply_demand_breakouts_rs_rating_desc RENAME TO idx_stocks_supply_demand_breakouts_momentum_score_desc" ) op.execute( "ALTER INDEX IF EXISTS idx_supply_demand_breakouts_rs_rating RENAME TO idx_supply_demand_breakouts_momentum_score" ) elif dialect_name == "sqlite": print("🗃️ SQLite: Recreating tables with new column names...") # SQLite doesn't support column renaming well, need to recreate tables # 1. Recreate mcp_maverick_stocks table print(" 📊 Recreating mcp_maverick_stocks...") op.rename_table("mcp_maverick_stocks", "mcp_maverick_stocks_old") op.create_table( "mcp_maverick_stocks", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_150", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("momentum_score", sa.Numeric(5, 2), default=0), # was rs_rating sa.Column("avg_vol_30d", sa.Numeric(15, 2), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr", sa.Numeric(12, 4), default=0), # Pattern Analysis sa.Column("pattern_type", sa.String(50)), sa.Column("squeeze_status", sa.String(50)), sa.Column("consolidation_status", sa.String(50)), # was vcp_status sa.Column("entry_signal", sa.String(50)), # Scoring sa.Column("compression_score", sa.Integer(), default=0), sa.Column("pattern_detected", sa.Integer(), default=0), sa.Column("combined_score", sa.Integer(), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data with column mapping op.execute(""" INSERT INTO mcp_maverick_stocks SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, ema_21, sma_50, sma_150, sma_200, rs_rating, avg_vol_30d, adr_pct, atr, pattern_type, squeeze_status, vcp_status, entry_signal, compression_score, pattern_detected, combined_score, created_at, updated_at FROM mcp_maverick_stocks_old """) op.drop_table("mcp_maverick_stocks_old") # Create indexes for maverick stocks op.create_index( "mcp_maverick_stocks_combined_score_idx", "mcp_maverick_stocks", ["combined_score"], ) op.create_index( "mcp_maverick_stocks_momentum_score_idx", "mcp_maverick_stocks", ["momentum_score"], ) op.create_index( "mcp_maverick_stocks_date_analyzed_idx", "mcp_maverick_stocks", ["date_analyzed"], ) op.create_index( "mcp_maverick_stocks_stock_date_idx", "mcp_maverick_stocks", ["stock_id", "date_analyzed"], ) # 2. Recreate mcp_maverick_bear_stocks table print(" 🐻 Recreating mcp_maverick_bear_stocks...") op.rename_table("mcp_maverick_bear_stocks", "mcp_maverick_bear_stocks_old") op.create_table( "mcp_maverick_bear_stocks", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("momentum_score", sa.Numeric(5, 2), default=0), # was rs_rating sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("rsi_14", sa.Numeric(5, 2), default=0), # MACD Indicators sa.Column("macd", sa.Numeric(12, 6), default=0), sa.Column("macd_signal", sa.Numeric(12, 6), default=0), sa.Column("macd_histogram", sa.Numeric(12, 6), default=0), # Bear Market Indicators sa.Column("dist_days_20", sa.Integer(), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr_contraction", sa.Boolean(), default=False), sa.Column("atr", sa.Numeric(12, 4), default=0), sa.Column("avg_vol_30d", sa.Numeric(15, 2), default=0), sa.Column("big_down_vol", sa.Boolean(), default=False), # Pattern Analysis sa.Column("squeeze_status", sa.String(50)), sa.Column("consolidation_status", sa.String(50)), # was vcp_status # Scoring sa.Column("score", sa.Integer(), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data with column mapping op.execute(""" INSERT INTO mcp_maverick_bear_stocks SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, rs_rating, ema_21, sma_50, sma_200, rsi_14, macd, macd_signal, macd_histogram, dist_days_20, adr_pct, atr_contraction, atr, avg_vol_30d, big_down_vol, squeeze_status, vcp_status, score, created_at, updated_at FROM mcp_maverick_bear_stocks_old """) op.drop_table("mcp_maverick_bear_stocks_old") # Create indexes for bear stocks op.create_index( "mcp_maverick_bear_stocks_score_idx", "mcp_maverick_bear_stocks", ["score"] ) op.create_index( "mcp_maverick_bear_stocks_momentum_score_idx", "mcp_maverick_bear_stocks", ["momentum_score"], ) op.create_index( "mcp_maverick_bear_stocks_date_analyzed_idx", "mcp_maverick_bear_stocks", ["date_analyzed"], ) op.create_index( "mcp_maverick_bear_stocks_stock_date_idx", "mcp_maverick_bear_stocks", ["stock_id", "date_analyzed"], ) # 3. Recreate mcp_supply_demand_breakouts table print(" 📈 Recreating mcp_supply_demand_breakouts...") op.rename_table( "mcp_supply_demand_breakouts", "mcp_supply_demand_breakouts_old" ) op.create_table( "mcp_supply_demand_breakouts", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_150", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("momentum_score", sa.Numeric(5, 2), default=0), # was rs_rating sa.Column("avg_volume_30d", sa.Numeric(15, 2), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr", sa.Numeric(12, 4), default=0), # Pattern Analysis sa.Column("pattern_type", sa.String(50)), sa.Column("squeeze_status", sa.String(50)), sa.Column("consolidation_status", sa.String(50)), # was vcp_status sa.Column("entry_signal", sa.String(50)), # Supply/Demand Analysis sa.Column("accumulation_rating", sa.Numeric(5, 2), default=0), sa.Column("distribution_rating", sa.Numeric(5, 2), default=0), sa.Column("breakout_strength", sa.Numeric(5, 2), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data with column mapping op.execute(""" INSERT INTO mcp_supply_demand_breakouts SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, ema_21, sma_50, sma_150, sma_200, rs_rating, avg_volume_30d, adr_pct, atr, pattern_type, squeeze_status, vcp_status, entry_signal, accumulation_rating, distribution_rating, breakout_strength, created_at, updated_at FROM mcp_supply_demand_breakouts_old """) op.drop_table("mcp_supply_demand_breakouts_old") # Create indexes for supply/demand breakouts op.create_index( "mcp_supply_demand_breakouts_momentum_score_idx", "mcp_supply_demand_breakouts", ["momentum_score"], ) op.create_index( "mcp_supply_demand_breakouts_date_analyzed_idx", "mcp_supply_demand_breakouts", ["date_analyzed"], ) op.create_index( "mcp_supply_demand_breakouts_stock_date_idx", "mcp_supply_demand_breakouts", ["stock_id", "date_analyzed"], ) op.create_index( "mcp_supply_demand_breakouts_ma_filter_idx", "mcp_supply_demand_breakouts", ["close_price", "sma_50", "sma_150", "sma_200"], ) # Log successful migration print("✅ Successfully removed proprietary terminology from database columns:") print(" - rs_rating → momentum_score (more descriptive)") print(" - vcp_status → consolidation_status (generic pattern description)") print(" - All related indexes have been updated") def downgrade(): """Revert column names back to original proprietary terms.""" bind = op.get_bind() dialect_name = bind.dialect.name if dialect_name == "postgresql": print("🗃️ PostgreSQL: Reverting column names...") # 1. Revert indexes first print(" 🔍 Reverting indexes...") op.execute( "ALTER INDEX IF EXISTS mcp_maverick_stocks_momentum_score_idx RENAME TO mcp_maverick_stocks_rs_rating_idx" ) op.execute( "ALTER INDEX IF EXISTS mcp_maverick_bear_stocks_momentum_score_idx RENAME TO mcp_maverick_bear_stocks_rs_rating_idx" ) op.execute( "ALTER INDEX IF EXISTS mcp_supply_demand_breakouts_momentum_score_idx RENAME TO mcp_supply_demand_breakouts_rs_rating_idx" ) # Revert any legacy indexes op.execute( "ALTER INDEX IF EXISTS idx_stocks_supply_demand_breakouts_momentum_score_desc RENAME TO idx_stocks_supply_demand_breakouts_rs_rating_desc" ) op.execute( "ALTER INDEX IF EXISTS idx_supply_demand_breakouts_momentum_score RENAME TO idx_supply_demand_breakouts_rs_rating" ) # 2. Revert columns in mcp_maverick_stocks print(" 📊 Reverting mcp_maverick_stocks...") op.alter_column( "mcp_maverick_stocks", "momentum_score", new_column_name="rs_rating" ) op.alter_column( "mcp_maverick_stocks", "consolidation_status", new_column_name="vcp_status" ) # 3. Revert columns in mcp_maverick_bear_stocks print(" 🐻 Reverting mcp_maverick_bear_stocks...") op.alter_column( "mcp_maverick_bear_stocks", "momentum_score", new_column_name="rs_rating" ) op.alter_column( "mcp_maverick_bear_stocks", "consolidation_status", new_column_name="vcp_status", ) # 4. Revert columns in mcp_supply_demand_breakouts print(" 📈 Reverting mcp_supply_demand_breakouts...") op.alter_column( "mcp_supply_demand_breakouts", "momentum_score", new_column_name="rs_rating" ) op.alter_column( "mcp_supply_demand_breakouts", "consolidation_status", new_column_name="vcp_status", ) elif dialect_name == "sqlite": print("🗃️ SQLite: Recreating tables with original column names...") # SQLite: Recreate tables with original names # 1. Recreate mcp_maverick_stocks table with original columns print(" 📊 Recreating mcp_maverick_stocks...") op.rename_table("mcp_maverick_stocks", "mcp_maverick_stocks_new") op.create_table( "mcp_maverick_stocks", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_150", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("rs_rating", sa.Numeric(5, 2), default=0), # restored sa.Column("avg_vol_30d", sa.Numeric(15, 2), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr", sa.Numeric(12, 4), default=0), # Pattern Analysis sa.Column("pattern_type", sa.String(50)), sa.Column("squeeze_status", sa.String(50)), sa.Column("vcp_status", sa.String(50)), # restored sa.Column("entry_signal", sa.String(50)), # Scoring sa.Column("compression_score", sa.Integer(), default=0), sa.Column("pattern_detected", sa.Integer(), default=0), sa.Column("combined_score", sa.Integer(), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data back with column mapping op.execute(""" INSERT INTO mcp_maverick_stocks SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, ema_21, sma_50, sma_150, sma_200, momentum_score, avg_vol_30d, adr_pct, atr, pattern_type, squeeze_status, consolidation_status, entry_signal, compression_score, pattern_detected, combined_score, created_at, updated_at FROM mcp_maverick_stocks_new """) op.drop_table("mcp_maverick_stocks_new") # Create original indexes op.create_index( "mcp_maverick_stocks_combined_score_idx", "mcp_maverick_stocks", ["combined_score"], ) op.create_index( "mcp_maverick_stocks_rs_rating_idx", "mcp_maverick_stocks", ["rs_rating"] ) op.create_index( "mcp_maverick_stocks_date_analyzed_idx", "mcp_maverick_stocks", ["date_analyzed"], ) op.create_index( "mcp_maverick_stocks_stock_date_idx", "mcp_maverick_stocks", ["stock_id", "date_analyzed"], ) # 2. Recreate mcp_maverick_bear_stocks with original columns print(" 🐻 Recreating mcp_maverick_bear_stocks...") op.rename_table("mcp_maverick_bear_stocks", "mcp_maverick_bear_stocks_new") op.create_table( "mcp_maverick_bear_stocks", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("rs_rating", sa.Numeric(5, 2), default=0), # restored sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("rsi_14", sa.Numeric(5, 2), default=0), # MACD Indicators sa.Column("macd", sa.Numeric(12, 6), default=0), sa.Column("macd_signal", sa.Numeric(12, 6), default=0), sa.Column("macd_histogram", sa.Numeric(12, 6), default=0), # Bear Market Indicators sa.Column("dist_days_20", sa.Integer(), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr_contraction", sa.Boolean(), default=False), sa.Column("atr", sa.Numeric(12, 4), default=0), sa.Column("avg_vol_30d", sa.Numeric(15, 2), default=0), sa.Column("big_down_vol", sa.Boolean(), default=False), # Pattern Analysis sa.Column("squeeze_status", sa.String(50)), sa.Column("vcp_status", sa.String(50)), # restored # Scoring sa.Column("score", sa.Integer(), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data back op.execute(""" INSERT INTO mcp_maverick_bear_stocks SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, momentum_score, ema_21, sma_50, sma_200, rsi_14, macd, macd_signal, macd_histogram, dist_days_20, adr_pct, atr_contraction, atr, avg_vol_30d, big_down_vol, squeeze_status, consolidation_status, score, created_at, updated_at FROM mcp_maverick_bear_stocks_new """) op.drop_table("mcp_maverick_bear_stocks_new") # Create original indexes op.create_index( "mcp_maverick_bear_stocks_score_idx", "mcp_maverick_bear_stocks", ["score"] ) op.create_index( "mcp_maverick_bear_stocks_rs_rating_idx", "mcp_maverick_bear_stocks", ["rs_rating"], ) op.create_index( "mcp_maverick_bear_stocks_date_analyzed_idx", "mcp_maverick_bear_stocks", ["date_analyzed"], ) op.create_index( "mcp_maverick_bear_stocks_stock_date_idx", "mcp_maverick_bear_stocks", ["stock_id", "date_analyzed"], ) # 3. Recreate mcp_supply_demand_breakouts with original columns print(" 📈 Recreating mcp_supply_demand_breakouts...") op.rename_table( "mcp_supply_demand_breakouts", "mcp_supply_demand_breakouts_new" ) op.create_table( "mcp_supply_demand_breakouts", sa.Column("id", sa.BigInteger(), primary_key=True, autoincrement=True), sa.Column( "stock_id", postgresql.UUID(as_uuid=True), nullable=False, index=True ), sa.Column("date_analyzed", sa.Date(), nullable=False), # OHLCV Data sa.Column("open_price", sa.Numeric(12, 4), default=0), sa.Column("high_price", sa.Numeric(12, 4), default=0), sa.Column("low_price", sa.Numeric(12, 4), default=0), sa.Column("close_price", sa.Numeric(12, 4), default=0), sa.Column("volume", sa.BigInteger(), default=0), # Technical Indicators sa.Column("ema_21", sa.Numeric(12, 4), default=0), sa.Column("sma_50", sa.Numeric(12, 4), default=0), sa.Column("sma_150", sa.Numeric(12, 4), default=0), sa.Column("sma_200", sa.Numeric(12, 4), default=0), sa.Column("rs_rating", sa.Numeric(5, 2), default=0), # restored sa.Column("avg_volume_30d", sa.Numeric(15, 2), default=0), sa.Column("adr_pct", sa.Numeric(5, 2), default=0), sa.Column("atr", sa.Numeric(12, 4), default=0), # Pattern Analysis sa.Column("pattern_type", sa.String(50)), sa.Column("squeeze_status", sa.String(50)), sa.Column("vcp_status", sa.String(50)), # restored sa.Column("entry_signal", sa.String(50)), # Supply/Demand Analysis sa.Column("accumulation_rating", sa.Numeric(5, 2), default=0), sa.Column("distribution_rating", sa.Numeric(5, 2), default=0), sa.Column("breakout_strength", sa.Numeric(5, 2), default=0), sa.Column("created_at", sa.DateTime(timezone=True), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False), ) # Copy data back op.execute(""" INSERT INTO mcp_supply_demand_breakouts SELECT id, stock_id, date_analyzed, open_price, high_price, low_price, close_price, volume, ema_21, sma_50, sma_150, sma_200, momentum_score, avg_volume_30d, adr_pct, atr, pattern_type, squeeze_status, consolidation_status, entry_signal, accumulation_rating, distribution_rating, breakout_strength, created_at, updated_at FROM mcp_supply_demand_breakouts_new """) op.drop_table("mcp_supply_demand_breakouts_new") # Create original indexes op.create_index( "mcp_supply_demand_breakouts_rs_rating_idx", "mcp_supply_demand_breakouts", ["rs_rating"], ) op.create_index( "mcp_supply_demand_breakouts_date_analyzed_idx", "mcp_supply_demand_breakouts", ["date_analyzed"], ) op.create_index( "mcp_supply_demand_breakouts_stock_date_idx", "mcp_supply_demand_breakouts", ["stock_id", "date_analyzed"], ) op.create_index( "mcp_supply_demand_breakouts_ma_filter_idx", "mcp_supply_demand_breakouts", ["close_price", "sma_50", "sma_150", "sma_200"], ) print("✅ Successfully reverted column names back to original proprietary terms")