bugfixing dividends
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MuslemRahimi
parent
cd388202a5
commit
f29cb1471c
@ -24,8 +24,10 @@ async def save_as_json(symbol, data, file_name):
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with open(f"{file_name}/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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async def get_data(ticker, con, etf_con, stock_symbols, etf_symbols):
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try:
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# Choose the appropriate table and column names
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if ticker in etf_symbols:
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table_name = 'etfs'
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column_name = 'etf_dividend'
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@ -33,68 +35,82 @@ async def get_data(ticker, con, etf_con, stock_symbols, etf_symbols):
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table_name = 'stocks'
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column_name = 'stock_dividend'
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# Build and execute the SQL query
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query_template = f"""
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SELECT {column_name}, quote
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SELECT {column_name}
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FROM {table_name}
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WHERE symbol = ?
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"""
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df = pd.read_sql_query(query_template,
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etf_con if table_name == 'etfs' else con,
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params=(ticker,))
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dividend_data = orjson.loads(df[column_name].iloc[0])
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res = dividend_data.get('historical', [])
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df = pd.read_sql_query(
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query_template,
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etf_con if table_name == 'etfs' else con,
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params=(ticker,)
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)
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# Load the JSON data
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res = orjson.loads(df[column_name].iloc[0])
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# Filter out records that do not have a recordDate or paymentDate
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filtered_res = [item for item in res if item['recordDate'] and item['paymentDate']]
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# Get the current and previous year
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today = datetime.today()
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current_year = str(today.year)
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previous_year = str(today.year - 1)
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# Compute the previous year's total dividend
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previous_year_records = [item for item in filtered_res if previous_year in item['recordDate']]
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previous_annual_dividend = round(sum(float(item['adjDividend']) for item in previous_year_records), 2) if previous_year_records else 0
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# Calculate payout frequency
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current_year_records = [item for item in filtered_res if current_year in item['recordDate']]
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record_dates = sorted([datetime.strptime(item['recordDate'], '%Y-%m-%d') for item in current_year_records])
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def map_frequency_to_standard(calculated_frequency):
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if calculated_frequency >= 45: # Approximately weekly
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return 5
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elif calculated_frequency >= 10: # More frequent than quarterly but less than weekly
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return 5 # Default to weekly for very frequent payments
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elif calculated_frequency >= 3: # Approximately quarterly
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return 4
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elif calculated_frequency >= 1.5: # Approximately semi-annual
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return 2
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else: # Annual or less frequent
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return 1
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if not filtered_res:
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raise ValueError("No valid dividend records found.")
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if len(record_dates) > 1:
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total_days = (record_dates[-1] - record_dates[0]).days
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intervals = len(record_dates) - 1
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average_interval = total_days / intervals if intervals > 0 else None
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raw_frequency = round(365 / average_interval) if average_interval and average_interval > 0 else len(record_dates)
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estimated_frequency = map_frequency_to_standard(raw_frequency)
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else:
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estimated_frequency = 1 # Default to annual if only one record exists
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# Extract payout frequency and dividend yield from the first valid record
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payout_frequency = filtered_res[0]['frequency']
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dividend_yield = filtered_res[0]['yield']
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# Process quote data
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quote_data = orjson.loads(df['quote'].iloc[0])[0]
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eps = quote_data.get('eps')
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current_price = quote_data.get('price')
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# Determine the period for the last year using the maximum record date
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max_record_date = max(datetime.fromisoformat(item['recordDate']) for item in filtered_res)
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one_year_ago = max_record_date - timedelta(days=365)
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# Calculate dividend growth rate
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# Sort records by record date
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sorted_records = sorted(filtered_res, key=lambda x: datetime.fromisoformat(x['recordDate']))
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dividend_yield = round((previous_annual_dividend / current_price) * 100, 2) if current_price else None
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payout_ratio = round((1 - (eps - previous_annual_dividend) / eps) * 100, 2) if eps else None
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# Get the year of the latest dividend
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latest_year = datetime.fromisoformat(sorted_records[-1]['recordDate']).year
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# Find the first dividend in the current year and the first dividend from previous year
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latest_dividend = None
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previous_year_dividend = None
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for record in sorted_records:
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record_date = datetime.fromisoformat(record['recordDate'])
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if record_date.year == latest_year and latest_dividend is None:
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latest_dividend = record['adjDividend']
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elif record_date.year == latest_year - 1 and previous_year_dividend is None:
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previous_year_dividend = record['adjDividend']
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# Break if we found both dividends
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if latest_dividend is not None and previous_year_dividend is not None:
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break
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# Calculate growth rate if both values exist
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dividend_growth = None
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if latest_dividend is not None and previous_year_dividend is not None and previous_year_dividend != 0:
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dividend_growth = round(((latest_dividend - previous_year_dividend) / previous_year_dividend) * 100, 2)
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# Sum up all adjDividend values for records in the last year
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annual_dividend = sum(
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item['adjDividend']
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for item in filtered_res
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if datetime.fromisoformat(item['recordDate']) >= one_year_ago
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)
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with open(f"json/quote/{ticker}.json","r") as file:
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try:
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quote_data = orjson.loads(file.read())
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eps = quote_data['eps']
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payout_ratio = round((1 - (eps - annual_dividend) / eps) * 100, 2) if eps else None
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except:
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payout_ratio = None
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return {
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'payoutFrequency': estimated_frequency,
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'annualDividend': previous_annual_dividend,
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'dividendYield': dividend_yield,
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'payoutRatio': payout_ratio,
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'dividendGrowth': None,
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'payoutFrequency': payout_frequency,
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'annualDividend': round(annual_dividend,2) if annual_dividend != None else annual_dividend,
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'dividendYield': round(dividend_yield,2) if dividend_yield != None else dividend_yield,
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'payoutRatio': round(payout_ratio,2) if payout_ratio != None else payout_ratio,
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'dividendGrowth': dividend_growth,
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'history': filtered_res,
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}
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@ -102,6 +118,7 @@ async def get_data(ticker, con, etf_con, stock_symbols, etf_symbols):
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print(f"Error processing ticker {ticker}: {e}")
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return {}
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async def run():
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con = sqlite3.connect('stocks.db')
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cursor = con.cursor()
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@ -114,7 +131,7 @@ async def run():
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etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
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etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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total_symbols = ['AAPL'] #stock_symbols + etf_symbols
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total_symbols = ['QQQY'] #stock_symbols + etf_symbols
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for ticker in tqdm(total_symbols):
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res = await get_data(ticker, con, etf_con, stock_symbols, etf_symbols)
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