File size: 3,753 Bytes
d53dc44 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | import sys, re
with open('main.py', 'r', encoding='utf-8') as f:
text = f.read()
old_bonus = ''' # Price Preference Bias: User prefers stocks < , then <
price_bonus = 0.0
current_price = float(df_5m["close"].iloc[-1])
if current_price < 50:
price_bonus = 0.50 # Huge boost for first preference
elif current_price < 100:
price_bonus = 0.25 # Moderate boost for second preference
# Alpha-weighted composite ranking score
rank_score = (
0.30 * signal_strength + # pure strategy score
0.25 * relative_strength + # RS vs SPY
0.20 * min(vol_adj_momentum, 1.0) + # vol-adjusted momentum
0.15 * volume_score + # volume confirmation
0.10 * trend_consistency + # higher-highs persistence
price_bonus # user price preference
)'''
new_bonus = ''' # Strict Price Filtering
current_price = float(df_5m["close"].iloc[-1])
if current_price >= 200:
logger.debug("%s skipped: price $%.2f >= limit", symbol, current_price)
continue
price_tier = 1 if current_price < 100 else 2
# Alpha-weighted composite ranking score
rank_score = (
0.30 * signal_strength + # pure strategy score
0.25 * relative_strength + # RS vs SPY
0.20 * min(vol_adj_momentum, 1.0) + # vol-adjusted momentum
0.15 * volume_score + # volume confirmation
0.10 * trend_consistency # higher-highs persistence
)'''
text = text.replace(old_bonus, new_bonus)
# Update candidates.append
old_append = ''' candidates.append({
"symbol": symbol,
"final": final,
"price_target": price_target,
"df_5m": df_5m,
"rank_score": rank_score,
"signal_strength": signal_strength,
"relative_strength": relative_strength,
"vol_adj_momentum": vol_adj_momentum,
"volume_score": volume_score,
"trend_consistency": trend_consistency,
"sector": _get_sector(symbol),
"atr_pct": atr_pct,
})'''
new_append = ''' candidates.append({
"symbol": symbol,
"final": final,
"price_target": price_target,
"df_5m": df_5m,
"rank_score": rank_score,
"signal_strength": signal_strength,
"relative_strength": relative_strength,
"vol_adj_momentum": vol_adj_momentum,
"volume_score": volume_score,
"trend_consistency": trend_consistency,
"sector": _get_sector(symbol),
"atr_pct": atr_pct,
"price_tier": price_tier,
"current_price": current_price,
})'''
text = text.replace(old_append, new_append)
# Fix the sorting!
old_sort = ''' candidates.sort(key=lambda c: c["rank_score"], reverse=True)'''
new_sort = ''' candidates.sort(key=lambda c: (c["price_tier"], -c["rank_score"]))'''
text = text.replace(old_sort, new_sort)
old_ml_sort = ''' candidates.sort(key=lambda c: c["final_rank"], reverse=True)'''
new_ml_sort = ''' candidates.sort(key=lambda c: (c["price_tier"], -c["final_rank"]))'''
text = text.replace(old_ml_sort, new_ml_sort)
with open('main.py', 'w', encoding='utf-8') as f:
f.write(text)
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