#!/bin/bash
# Test script for Phase 140: Equity Screener
echo "======================================================================"
echo "PHASE 140 TEST: Equity Screener (Multi-Factor)"
echo "======================================================================"
echo ""
cd /home/quant/apps/quantclaw-data
echo "Test 1: List available factors"
echo "------------------------------"
python3 cli.py factors 2>&1 | head -15
echo ""
echo "Test 2: List available presets"
echo "------------------------------"
python3 cli.py presets 2>&1 | head -20
echo ""
echo "Test 3: Calculate factors for single stock"
echo "------------------------------"
python3 -c "
from modules.equity_screener import EquityScreener
screener = EquityScreener(['AAPL'])
factors = screener.calculate_factors('AAPL')
if factors:
print('✓ AAPL factors calculated successfully')
print(f' Company: {factors[\"company_name\"]}')
print(f' Sector: {factors[\"sector\"]}')
print(f' P/E: {factors[\"pe\"]}')
print(f' P/B: {factors[\"pb\"]}')
print(f' ROE: {factors[\"roe\"]}')
print(f' Return 3M: {factors[\"return_3m\"]}')
print(f' RSI: {factors[\"rsi\"]}')
else:
print('✗ Failed to calculate factors')
" 2>&1
echo ""
echo "Test 4: Screen with custom filters (P/E < 30, ROE > 0.15)"
echo "------------------------------"
python3 -c "
from modules.equity_screener import EquityScreener
screener = EquityScreener(['AAPL', 'MSFT', 'GOOGL', 'JPM', 'WMT', 'PG'])
results = screener.screen({'pe': (0, 30), 'roe': (0.15, None)}, limit=5)
print(f'✓ Found {len(results)} stocks matching criteria:')
for r in results:
print(f' - {r[\"ticker\"]}: P/E={r[\"pe\"]:.2f}, ROE={r[\"roe\"]:.2f}')
" 2>&1
echo ""
echo "Test 5: Rank stocks by composite score"
echo "------------------------------"
python3 -c "
from modules.equity_screener import EquityScreener
import pandas as pd
screener = EquityScreener(['AAPL', 'MSFT', 'GOOGL', 'JPM', 'WMT'])
ranked = screener.rank_stocks(factors=['pe', 'roe', 'return_3m'])
print(f'✓ Ranked {len(ranked)} stocks by composite score:')
for idx, row in ranked.head(5).iterrows():
score = int(row['score']*100)
ticker = row['ticker']
pe = row.get('pe')
roe = row.get('roe')
print(f' {score}% - {ticker}: P/E={pe:.2f}, ROE={roe:.2f}' if pd.notna(pe) and pd.notna(roe) else f' {score}% - {ticker}')
" 2>&1
echo ""
echo "Test 6: Test value preset filter"
echo "------------------------------"
python3 -c "
from modules.equity_screener import SCREENING_PRESETS
preset = SCREENING_PRESETS['value']
print(f'✓ Value preset: {preset[\"name\"]}')
print(' Filters:')
for factor, (min_val, max_val) in preset['filters'].items():
min_str = f'{min_val}' if min_val is not None else '-∞'
max_str = f'{max_val}' if max_val is not None else '+∞'
print(f' - {factor}: [{min_str}, {max_str}]')
" 2>&1
echo ""
echo "Test 7: Count factor categories"
echo "------------------------------"
python3 -c "
from modules.equity_screener import FACTOR_CATEGORIES
total = sum(len(v) for v in FACTOR_CATEGORIES.values())
print(f'✓ Total screening factors: {total}')
for category, factors in FACTOR_CATEGORIES.items():
print(f' - {category}: {len(factors)} factors')
" 2>&1
echo ""
echo "======================================================================"
echo "✓ PHASE 140 TESTS COMPLETE"
echo "======================================================================"
echo ""
echo "Module: modules/equity_screener.py (610 LOC)"
echo "CLI Commands: screen, preset, rank, factors, presets"
echo "MCP Tools: screen_stocks, screen_preset, rank_stocks, screen_factors, screen_presets_list"
echo ""
echo "Features:"
echo " - 50+ screening factors across 6 categories"
echo " - Support for 8000+ US stocks (expandable)"
echo " - Custom filter combinations (AND logic)"
echo " - 5 predefined presets (value, growth, momentum, dividend, quality)"
echo " - Multi-factor ranking with composite scoring"
echo " - JSON output for all commands"
echo ""
