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update cron job

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This commit is contained in:
MuslemRahimi 2025-03-13 13:02:13 +01:00
parent 355e67d982
commit a0aa080cf4
2 changed files with 8 additions and 6 deletions
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10
app/cron_ai_score.py
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@ -311,8 +311,8 @@ async def fine_tune_and_evaluate(ticker, con, start_date, end_date, skip_downloa
if (data['precision'] >= 50 and data['accuracy'] >= 50 and
data['accuracy'] < 100 and data['precision'] < 100 and
data['f1_score'] >= 50 and data['recall_score'] >= 50 and
data['roc_auc_score'] >= 50):
data['f1_score'] >= 20 and data['recall_score'] >= 20 and
data['roc_auc_score'] >= 50) and len(data.get('backtest',[])) > 0:
await save_json(ticker, data)
data['backtest'] = [
{'date': entry['date'], 'yTest': entry['y_test'], 'yPred': entry['y_pred'], 'score': entry['score']}
@ -346,11 +346,13 @@ async def run():
if train_mode:
# Warm start training
stock_symbols = cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E6 AND symbol NOT LIKE '%.%'") #list(set(['CB','LOW','PFE','RTX','DIS','MS','BHP','BAC','PG','BABA','ACN','TMO','LLY','XOM','JPM','UNH','COST','HD','ASML','BRK-A','BRK-B','CAT','TT','SAP','APH','CVS','NOG','DVN','COP','OXY','MRO','MU','AVGO','INTC','LRCX','PLD','AMT','JNJ','ACN','TSM','V','ORCL','MA','BAC','BA','NFLX','ADBE','IBM','GME','NKE','ANGO','PNW','SHEL','XOM','WMT','BUD','AMZN','PEP','AMD','NVDA','AWR','TM','AAPL','GOOGL','META','MSFT','LMT','TSLA','DOV','PG','KO']))
stock_symbols = cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E6 AND symbol NOT LIKE '%.%'") #list(set(['CB','LOW','PFE','RTX','DIS','MS','BHP','BAC','PG','BABA','ACN','TMO','LLY','XOM','JPM','UNH','COST','HD','ASML','BRK-A','BRK-B','CAT','TT','SAP','APH','CVS','NOG','DVN','COP','OXY','MRO','MU','AVGO','INTC','LRCX','PLD','AMT','JNJ','ACN','TSM','V','ORCL','MA','BAC','BA','NFLX','ADBE','IBM','GME','NKE','ANGO','PNW','SHEL','XOM','WMT','BUD','AMZN','PEP','AMD','NVDA','AWR','TM','AAPL','GOOGL','META','MSFT','LMT','TSLA','DOV','PG','KO']))
stock_symbols = [row[0] for row in cursor.fetchall()]
#Test Mode
#stock_symbols = ['AAPL','TSLA']
print('Training for:', len(stock_symbols))
print('Training for', len(stock_symbols))
predictor = await warm_start_training(stock_symbols, con, skip_downloading, save_data)
#else:

4
app/ml_models/score_model.py
View File

@ -22,8 +22,8 @@ class ScorePredictor:
self.model = lgb.LGBMClassifier(
n_estimators=1_000,
learning_rate=0.001,
max_depth=10,
num_leaves=2**10-1,
max_depth=12,
num_leaves=2**12-1,
n_jobs=10,
random_state=42
)