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bugfixing

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This commit is contained in:
MuslemRahimi 2024-10-08 10:59:25 +02:00
parent 80d95da43b
commit 57a18fbf9e
6 changed files with 24 additions and 86 deletions
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27
app/cron_ai_score.py
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@ -80,7 +80,7 @@ def top_uncorrelated_features(df, target_col='Target', top_n=10, threshold=0.75)
selected_features.append(feature)
return selected_features
async def download_data(ticker, con, start_date, end_date, skip_downloading):
async def download_data(ticker, con, start_date, end_date, skip_downloading, save_data):
file_path = f"ml_models/training_data/ai-score/{ticker}.json"
@ -201,6 +201,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
'totalLiabilitiesAndStockholdersEquity', 'totalStockholdersEquity', 'totalInvestments','totalAssets',
]
# Function to compute combinations within a group
def compute_column_ratios(columns, df, new_columns):
column_combinations = list(combinations(columns, 2))
@ -240,7 +241,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
df_copy = df_combined.copy().map(lambda x: round(x, 2) if isinstance(x, float) else x)
# Save to a file if there are rows in the DataFrame
if not df_copy.empty:
if not df_copy.empty and save_data == True:
with open(file_path, 'wb') as file:
file.write(orjson.dumps(df_copy.to_dict(orient='records')))
@ -251,7 +252,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
pass
async def chunked_gather(tickers, con, skip_downloading, chunk_size):
async def chunked_gather(tickers, con, skip_downloading, save_data, chunk_size):
test_size = 0.2
start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
@ -267,7 +268,7 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
for chunk in tqdm(chunks(tickers, chunk_size)):
# Create tasks for each chunk
print(f"chunk size: {len(chunk)}")
tasks = [download_data(ticker, con, start_date, end_date, skip_downloading) for ticker in chunk]
tasks = [download_data(ticker, con, start_date, end_date, skip_downloading, save_data) for ticker in chunk]
# Await the results for the current chunk
chunk_results = await asyncio.gather(*tasks)
@ -309,18 +310,18 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
print(f'Overall Evaluation Metrics: {data}')
async def warm_start_training(tickers, con, skip_downloading):
async def warm_start_training(tickers, con, skip_downloading, save_data):
dfs = await chunked_gather(tickers, con, skip_downloading, chunk_size=100)
dfs = await chunked_gather(tickers, con, skip_downloading, save_data, chunk_size=100)
async def fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading):
async def fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading, save_data):
try:
df_train = pd.DataFrame()
df_test_dict = {} # Store test data for each ticker
all_test_data = [] # Store all test data for overall evaluation
df = await download_data(ticker, con, start_date, end_date, skip_downloading)
df = await download_data(ticker, con, start_date, end_date, skip_downloading, save_data)
split_size = int(len(df) * (1 - test_size))
df_train = df.iloc[:split_size]
df_test = df.iloc[split_size:]
@ -345,22 +346,24 @@ async def fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, s
# Save the evaluation data to a JSON file
await save_json(ticker, data)
print(f"Saved results for {ticker}")
except:
except Exception as e:
print(e)
pass
async def run():
train_mode = False # Set this to False for fine-tuning and evaluation
skip_downloading = False
save_data = train_mode
con = sqlite3.connect('stocks.db')
cursor = con.cursor()
cursor.execute("PRAGMA journal_mode = wal")
if train_mode:
# Warm start training
warm_start_symbols = list(set(['APO','UNM','CVS','SAVE','SIRI','EA','TTWO','NTDOY','GRC','ODP','IMAX','YUM','UPS','FI','DE','MDT','INFY','ICE','SNY','HON','BSX','C','ADP','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']))
warm_start_symbols = 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']))
print(f'Warm Start Training: Total Tickers {len(warm_start_symbols)}')
await warm_start_training(warm_start_symbols, con, skip_downloading)
await warm_start_training(warm_start_symbols, con, skip_downloading, save_data)
else:
start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
@ -374,7 +377,7 @@ async def run():
""")
stock_symbols = [row[0] for row in cursor.fetchall()]
for ticker in tqdm(stock_symbols):
await fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading)
await fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading, save_data)
con.close()

2
app/cron_options_gex.py
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@ -367,7 +367,7 @@ etf_cursor.execute("PRAGMA journal_mode = wal")
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
etf_symbols = [row[0] for row in etf_cursor.fetchall()]
total_symbols = ['SPY'] #stock_symbols + etf_symbols
total_symbols = stock_symbols + etf_symbols
query_template = """
SELECT date, close,change_percent

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app/ml_models/__pycache__/score_model.cpython-310.pyc
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54
app/ml_models/score_model.py
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@ -1,14 +1,8 @@
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from xgboost import XGBClassifier
from sklearn.ensemble import StackingClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
import lightgbm as lgb
@ -24,51 +18,18 @@ import os
class ScorePredictor:
def __init__(self):
self.scaler = StandardScaler()
self.scaler = MinMaxScaler()
self.pca = PCA(n_components=0.95)
# Define base models
self.xgb_model = XGBClassifier(
n_estimators=100,
max_depth=10,
learning_rate=0.001,
random_state=42,
n_jobs=10,
tree_method='gpu_hist',
)
'''
self.lgb_model = lgb.LGBMClassifier(
n_estimators=100,
n_estimators=2000,
learning_rate=0.001,
max_depth=10,
n_jobs=10
)
'''
self.rf_model = RandomForestClassifier(
n_estimators=100,
max_depth=10,
random_state=42,
max_depth=5,
num_leaves=2**5-1,
n_jobs=10
)
self.svc_model = SVC(probability=True, kernel='rbf')
self.knn_model = KNeighborsClassifier(n_neighbors=5)
self.nb_model = GaussianNB()
# Stacking ensemble (XGBoost + LightGBM) with Logistic Regression as meta-learner
self.model = StackingClassifier(
estimators=[
('xgb', self.xgb_model),
#('lgb', self.lgb_model),
('rf', self.rf_model),
('svc', self.svc_model),
('knn', self.knn_model),
('nb', self.nb_model)
],
final_estimator=LogisticRegression(),
n_jobs=10
)
self.warm_start_model_path = 'ml_models/weights/ai-score/stacking_weights.pkl'
@ -87,8 +48,8 @@ class ScorePredictor:
def warm_start_training(self, X_train, y_train):
X_train = self.preprocess_train_data(X_train)
if os.path.exists(self.warm_start_model_path):
with open(self.warm_start_model_path, 'rb') as f:
self.model = pickle.load(f)
os.remove(self.warm_start_model_path)
self.model.fit(X_train, y_train)
pickle.dump(self.model, open(self.warm_start_model_path, 'wb'))
print("Warm start model saved.")
@ -121,6 +82,7 @@ class ScorePredictor:
print(f"ROC AUC: {round(test_roc_auc_score * 100)}%")
last_prediction_prob = class_1_probabilities[-1]
print(pd.DataFrame({'y_test': y_test, 'y_pred': binary_predictions}))
print(f"Last prediction probability: {last_prediction_prob}")
thresholds = [0.8, 0.75, 0.7, 0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0]

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app/utils/__pycache__/feature_engineering.cpython-310.pyc
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27
app/utils/feature_engineering.py
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@ -177,33 +177,6 @@ def generate_statistical_features(df, windows=[20,50,200], price_col='close',
df_features[f'volume_skew_{window}'] = df[volume_col].rolling(window=window).skew()
df_features[f'volume_kurt_{window}'] = df[volume_col].rolling(window=window).kurt()
# Price-volume correlations
df_features[f'price_volume_corr_{window}'] = (
df[price_col].rolling(window=window)
.corr(df[volume_col]))
# Higher-order moments of returns
returns = df[price_col].pct_change()
df_features[f'returns_skew_{window}'] = returns.rolling(window=window).skew()
df_features[f'returns_kurt_{window}'] = returns.rolling(window=window).kurt()
# Cross-sectional statistics
df_features['price_acceleration'] = df[price_col].diff().diff()
df_features['returns_acceleration'] = df[price_col].pct_change().diff()
# Advanced volatility estimators
df_features['parkinson_vol'] = np.sqrt(
1/(4*np.log(2)) * (np.log(df[high_col]/df[low_col])**2))
df_features['garman_klass_vol'] = np.sqrt(
0.5 * np.log(df[high_col]/df[low_col])**2 -
(2*np.log(2)-1) * np.log(df[price_col]/df['open'])**2
)
# Dispersion measures
df_features['price_range'] = df[high_col] - df[low_col]
df_features['price_range_pct'] = df_features['price_range'] / df[price_col]
# Clean up any NaN values
df_features = df_features.dropna()