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bugfixing

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This commit is contained in:
MuslemRahimi 2024-09-24 19:08:08 +02:00
parent f1ddcd2003
commit 0e6751c149
1 changed files with 15 additions and 25 deletions
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40
app/cron_fomc_impact.py
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@ -72,25 +72,22 @@ async def run():
fomc_dates = await get_fomc_data() # Assumed to return the list of dictionaries as provided fomc_dates = await get_fomc_data() # Assumed to return the list of dictionaries as provided
start_date = datetime.now() - timedelta(days=365) start_date = datetime.now() - timedelta(days=365)
end_date = datetime.now() end_date = datetime.now()
# Extracting the dates for filtering # Extracting the dates for filtering
fomc_dates_list = [datetime.strptime(fomc['date'], '%Y-%m-%d').date() for fomc in fomc_dates] fomc_dates_list = [datetime.strptime(fomc['date'], '%Y-%m-%d').date() for fomc in fomc_dates]
# Connect to SQLite databases # Connect to SQLite databases
stock_con = sqlite3.connect('stocks.db') stock_con = sqlite3.connect('stocks.db')
etf_con = sqlite3.connect('etf.db') etf_con = sqlite3.connect('etf.db')
stock_cursor = stock_con.cursor() stock_cursor = stock_con.cursor()
stock_cursor.execute("PRAGMA journal_mode = wal") stock_cursor.execute("PRAGMA journal_mode = wal")
stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND marketCap >= 500E6") stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND marketCap >= 500E6")
stock_symbols = [row[0] for row in stock_cursor.fetchall()] stock_symbols = [row[0] for row in stock_cursor.fetchall()]
etf_cursor = etf_con.cursor() etf_cursor = etf_con.cursor()
etf_cursor.execute("PRAGMA journal_mode = wal") etf_cursor.execute("PRAGMA journal_mode = wal")
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs") etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
etf_symbols = [row[0] for row in etf_cursor.fetchall()] etf_symbols = [row[0] for row in etf_cursor.fetchall()]
total_symbols = stock_symbols + etf_symbols total_symbols = stock_symbols + etf_symbols
for ticker in tqdm(total_symbols): for ticker in tqdm(total_symbols):
try: try:
query = query_template.format(ticker=ticker) query = query_template.format(ticker=ticker)
@ -100,14 +97,11 @@ async def run():
if len(df_price) > 150 and len(fomc_dates) > 0: if len(df_price) > 150 and len(fomc_dates) > 0:
# Convert 'date' column in df_price to datetime.date for comparison # Convert 'date' column in df_price to datetime.date for comparison
df_price['date'] = pd.to_datetime(df_price['date']).dt.date df_price['date'] = pd.to_datetime(df_price['date']).dt.date
# Filter out every fifth row, unless the date is in fomc_dates # Filter out every fifth row, unless the date is in fomc_dates
filtered_df = df_price[ filtered_df = df_price[
(df_price.index % 5 != 0) | (df_price['date'].isin(fomc_dates_list)) (df_price.index % 5 != 0) | (df_price['date'].isin(fomc_dates_list))
] ]
filtered_df['date'] = filtered_df['date'].apply(lambda x: x.strftime('%Y-%m-%d')) filtered_df['date'] = filtered_df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))
# Prepare the result with filtered data and original fomc_dates # Prepare the result with filtered data and original fomc_dates
fomc_data_unique = {} fomc_data_unique = {}
for fomc in fomc_dates: for fomc in fomc_dates:
@ -120,36 +114,32 @@ async def run():
'actual': fomc['actual'], 'actual': fomc['actual'],
'estimate': fomc['estimate'] 'estimate': fomc['estimate']
} }
# Convert the unique FOMC data back to a list # Convert the unique FOMC data back to a list
res = { res = {
'fomcData': list(fomc_data_unique.values()), # Ensure unique dates 'fomcData': list(fomc_data_unique.values()), # Ensure unique dates
'history': filtered_df.to_dict('records') 'history': filtered_df.to_dict('records')
} }
# Compute percentage changes for FOMC dates # Compute percentage changes for FOMC dates
for i in range(len(res['fomcData']) - 1): for i in range(len(res['fomcData'])):
current_fomc_date = res['fomcData'][i]['date'] current_fomc_date = res['fomcData'][i]['date']
next_fomc_date = res['fomcData'][i + 1]['date']
# Find closing prices for the current and next FOMC dates
current_price_row = filtered_df[filtered_df['date'] == current_fomc_date] current_price_row = filtered_df[filtered_df['date'] == current_fomc_date]
next_price_row = filtered_df[filtered_df['date'] == next_fomc_date] if i == len(res['fomcData']) - 1:
# This is the last FOMC date, so compare it to the last price in the dataframe
if not current_price_row.empty and not next_price_row.empty: last_price_row = filtered_df.iloc[-1]
current_price = current_price_row['close'].values[0] current_price = current_price_row['close'].values[0]
next_price = next_price_row['close'].values[0] next_price = last_price_row['close']
else:
# Calculate the percentage change next_fomc_date = res['fomcData'][i + 1]['date']
percentage_change = ((next_price - current_price) / current_price) * 100 next_price_row = filtered_df[filtered_df['date'] == next_fomc_date]
res['fomcData'][i]['changePercentage'] = round(percentage_change,2) # Update with the new change percentage if not current_price_row.empty and not next_price_row.empty:
current_price = current_price_row['close'].values[0]
next_price = next_price_row['close'].values[0]
# Calculate the percentage change
percentage_change = ((next_price - current_price) / current_price) * 100
res['fomcData'][i]['changePercentage'] = round(percentage_change, 2) # Update with the new change percentage
await save_json(ticker, res) await save_json(ticker, res)
except Exception as e: except Exception as e:
print(f"Error processing {ticker}: {e}") print(f"Error processing {ticker}: {e}")
# Run the asyncio event loop # Run the asyncio event loop
loop = asyncio.get_event_loop() loop = asyncio.get_event_loop()
loop.run_until_complete(run()) loop.run_until_complete(run())