AmineTMIRI/backend
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bugfixing

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This commit is contained in:
MuslemRahimi 2025-03-13 15:51:04 +01:00
parent a0aa080cf4
commit d870181161
2 changed files with 9 additions and 6 deletions
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12
app/cron_ai_score.py
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@ -83,7 +83,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading, sav
] ]
# Async loading and filtering # Async loading and filtering
ignore_keys = ["symbol", "reportedCurrency", "calendarYear", "fillingDate", "acceptedDate", "period", "cik", "link", "finalLink","pbRatio","ptbRatio"] ignore_keys = ["symbol", "reportedCurrency", "calendarYear", "fillingDate", "acceptedDate", "period", "cik", "link", "finalLink","pbRatio","ptbRatio","grahamNumber"]
async def load_and_filter_json(path): async def load_and_filter_json(path):
async with aiofiles.open(path, 'r') as f: async with aiofiles.open(path, 'r') as f:
data = orjson.loads(await f.read()) data = orjson.loads(await f.read())
@ -133,7 +133,6 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading, sav
# Concatenate df with the filtered df_stats and df_ta # Concatenate df with the filtered df_stats and df_ta
df = pd.concat([df, df_ta_filtered, df_stats_filtered], axis=1) df = pd.concat([df, df_ta_filtered, df_stats_filtered], axis=1)
# Set up a dictionary for faster lookup of close prices and columns by date # Set up a dictionary for faster lookup of close prices and columns by date
df_dict = df.set_index('date').to_dict(orient='index') df_dict = df.set_index('date').to_dict(orient='index')
@ -167,7 +166,11 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading, sav
# Sort the combined data by date # Sort the combined data by date
combined_data = sorted(combined_data, key=lambda x: x['date']) combined_data = sorted(combined_data, key=lambda x: x['date'])
# Convert combined data to a DataFrame and drop rows with NaN values # Convert combined data to a DataFrame and drop rows with NaN values
df_combined = pd.DataFrame(combined_data).dropna() df_combined = pd.DataFrame(combined_data)
#nan_columns = df_combined.isna().sum()
#print(nan_columns[nan_columns > 0]) # Show only columns with NaNs
fundamental_columns = [ fundamental_columns = [
'revenue', 'costOfRevenue', 'grossProfit', 'netIncome', 'operatingIncome', 'operatingExpenses', 'revenue', 'costOfRevenue', 'grossProfit', 'netIncome', 'operatingIncome', 'operatingExpenses',
@ -302,7 +305,6 @@ async def fine_tune_and_evaluate(ticker, con, start_date, end_date, skip_downloa
test_data = df.iloc[split_size:] test_data = df.iloc[split_size:]
#selected_features = [col for col in df.columns if col not in ['date','price','Target']] #selected_features = [col for col in df.columns if col not in ['date','price','Target']]
# Fine-tune the model # Fine-tune the model
predictor = ScorePredictor() predictor = ScorePredictor()
#predictor.fine_tune_model(train_data[selected_features], train_data['Target']) #predictor.fine_tune_model(train_data[selected_features], train_data['Target'])
@ -350,7 +352,7 @@ async def run():
stock_symbols = [row[0] for row in cursor.fetchall()] stock_symbols = [row[0] for row in cursor.fetchall()]
#Test Mode #Test Mode
#stock_symbols = ['AAPL','TSLA'] #stock_symbols = ['MCD']
print('Training for', len(stock_symbols)) print('Training for', len(stock_symbols))
predictor = await warm_start_training(stock_symbols, con, skip_downloading, save_data) predictor = await warm_start_training(stock_symbols, con, skip_downloading, save_data)

1
app/main.py
View File

@ -4284,6 +4284,7 @@ async def get_data(data:TickerData, api_key: str = Security(get_api_key)):
except: except:
res = {} res = {}
print(res)
data = orjson.dumps(res) data = orjson.dumps(res)
compressed_data = gzip.compress(data) compressed_data = gzip.compress(data)