diff --git a/app/cron_dividends.py b/app/cron_dividends.py index 166e9eb..0a075ee 100644 --- a/app/cron_dividends.py +++ b/app/cron_dividends.py @@ -20,7 +20,7 @@ today = datetime.now(ny_tz).replace(hour=0, minute=0, second=0, microsecond=0) N_days_ago = today - timedelta(days=10) -async def save_as_json(symbol, data,file_name): +async def save_as_json(symbol, data, file_name): with open(f"{file_name}/{symbol}.json", 'w') as file: ujson.dump(data, file) @@ -54,32 +54,65 @@ async def get_data(ticker, con, etf_con, stock_symbols, etf_symbols): df = pd.read_sql_query(query_template, etf_con if table_name == 'etfs' else con, params=(ticker,)) dividend_data = orjson.loads(df[column_name].iloc[0]) - res = dividend_data.get('historical', []) - - filtered_res = [item for item in res if item['recordDate'] != '' and item['paymentDate'] != ''] - - # Calculate payout frequency based on dividends recorded in 2023 - payout_frequency = sum(1 for item in filtered_res if '2023' in item['recordDate']) + filtered_res = [item for item in res if item['recordDate'] and item['paymentDate']] + + # Dynamically compute the current and previous year based on New York timezone + current_year = str(datetime.now(ny_tz).year) + previous_year = str(datetime.now(ny_tz).year - 1) + + # Filter records for the current year + current_year_records = [item for item in filtered_res if current_year in item['recordDate']] + dividends_current_year = [float(item['adjDividend']) for item in current_year_records] + + # Compute the estimated payout frequency using the intervals between record dates + record_dates = [] + for item in current_year_records: + try: + record_date = datetime.strptime(item['recordDate'], '%Y-%m-%d') + record_dates.append(record_date) + except Exception as e: + continue + record_dates.sort() + + if len(record_dates) > 1: + total_days = (record_dates[-1] - record_dates[0]).days + intervals = len(record_dates) - 1 + average_interval = total_days / intervals if intervals > 0 else None + estimated_frequency = round(365 / average_interval) if average_interval and average_interval > 0 else len(record_dates) + else: + # If there's only one record, assume weekly (52 payments) as a fallback; + # if no record exists, frequency remains 0. + estimated_frequency = 52 if record_dates else 0 + + # Project the annual dividend using the average dividend amount + if dividends_current_year: + avg_dividend = sum(dividends_current_year) / len(dividends_current_year) + annual_dividend = round(avg_dividend * estimated_frequency, 2) + else: + annual_dividend = 0 + + # For the previous year, assume the data is complete and sum the dividends + dividends_previous_year = [ + float(item['adjDividend']) + for item in filtered_res + if previous_year in item['recordDate'] + ] + previous_annual_dividend = round(sum(dividends_previous_year), 2) if dividends_previous_year else 0 + quote_data = orjson.loads(df['quote'].iloc[0])[0] eps = quote_data.get('eps') current_price = quote_data.get('price') - - amount = filtered_res[0]['adjDividend'] if filtered_res else 0 - annual_dividend = round(amount * payout_frequency, 2) + dividend_yield = round((annual_dividend / current_price) * 100, 2) if current_price else None - payout_ratio = round((1 - (eps - annual_dividend) / eps) * 100, 2) if eps else None - - previous_index = next((i for i, item in enumerate(filtered_res) if '2023' in item['recordDate']), None) - - # Calculate previousAnnualDividend and dividendGrowth - previous_annual_dividend = (filtered_res[previous_index]['adjDividend'] * payout_frequency) if previous_index is not None else 0 - dividend_growth = round(((annual_dividend - previous_annual_dividend) / previous_annual_dividend) * 100, 2) if previous_annual_dividend else None - - + dividend_growth = ( + round(((annual_dividend - previous_annual_dividend) / previous_annual_dividend) * 100, 2) + if previous_annual_dividend else None + ) + return { - 'payoutFrequency': payout_frequency, + 'payoutFrequency': estimated_frequency, 'annualDividend': annual_dividend, 'dividendYield': dividend_yield, 'payoutRatio': payout_ratio, @@ -87,55 +120,11 @@ async def get_data(ticker, con, etf_con, stock_symbols, etf_symbols): 'history': filtered_res, } - except: - res = {} - - return res - - - -async def get_dividends_announcement(session, ticker, stock_symbols): - querystring = {"token": api_key, "parameters[tickers]": ticker} - ny_tz = pytz.timezone('America/New_York') - today = ny_tz.localize(datetime.now()) - N_days_ago = today - timedelta(days=30) # Example, adjust as needed - - try: - async with session.get(url, params=querystring, headers=headers) as response: - if response.status == 200: - data = ujson.loads(await response.text())['dividends'] - recent_dates = [item for item in data if N_days_ago <= ny_tz.localize(datetime.strptime(item["date"], "%Y-%m-%d")) <= today] - if recent_dates: - nearest_recent = min(recent_dates, key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d")) - try: - symbol = nearest_recent['ticker'] - dividend = float(nearest_recent['dividend']) if nearest_recent['dividend'] != '' else 0 - dividend_prior = float(nearest_recent['dividend_prior']) if nearest_recent['dividend_prior'] != '' else 0 - dividend_yield = round(float(nearest_recent['dividend_yield']) * 100, 2) if nearest_recent['dividend_yield'] != '' else 0 - ex_dividend_date = nearest_recent['ex_dividend_date'] if nearest_recent['ex_dividend_date'] != '' else 0 - payable_date = nearest_recent['payable_date'] if nearest_recent['payable_date'] != '' else 0 - record_date = nearest_recent['record_date'] if nearest_recent['record_date'] != '' else 0 - if symbol in stock_symbols and dividend != 0 and payable_date != 0 and dividend_prior != 0 and ex_dividend_date != 0 and record_date != 0 and dividend_yield != 0: - res_dict = { - 'symbol': symbol, - 'date': nearest_recent['date'], - 'dividend': dividend, - 'dividendPrior': dividend_prior, - 'dividendYield': dividend_yield, - 'exDividendDate': ex_dividend_date, - 'payableDate': payable_date, - 'recordDate': record_date, - } - await save_as_json(symbol, res_dict,'json/dividends/announcement') - except Exception as e: - # Log or handle the exception - print(e) - except: - pass - + except Exception as e: + print(f"Error processing ticker {ticker}: {e}") + return {} async def run(): - con = sqlite3.connect('stocks.db') cursor = con.cursor() cursor.execute("PRAGMA journal_mode = wal") @@ -152,23 +141,15 @@ async def run(): for ticker in tqdm(total_symbols): res = await get_data(ticker, con, etf_con, stock_symbols, etf_symbols) try: - if len(res.get('history')) > 0 and res.get('dividendGrowth') != None: + if len(res.get('history', [])) > 0: + print(res) await save_as_json(ticker, res, 'json/dividends/companies') - except: - pass + except Exception as e: + print(f"Error saving data for {ticker}: {e}") - con.close() etf_con.close() - delete_files_in_directory("json/dividends/announcement") - - async with aiohttp.ClientSession() as session: - tasks = [get_dividends_announcement(session, symbol, stock_symbols) for symbol in stock_symbols] - for f in tqdm(asyncio.as_completed(tasks), total=len(stock_symbols)): - await f - - try: asyncio.run(run()) except Exception as e: