146 lines
6.3 KiB
Python
146 lines
6.3 KiB
Python
from datetime import datetime, timedelta
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import ujson
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import asyncio
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import aiohttp
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import os
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from dotenv import load_dotenv
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import sqlite3
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import pandas as pd
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from tqdm import tqdm
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# Load environment variables
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load_dotenv()
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api_key = os.getenv('FMP_API_KEY')
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query_template = """
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SELECT date, close
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FROM "{ticker}"
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WHERE date BETWEEN ? AND ?
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"""
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# Function to save JSON data
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async def save_json(symbol, data):
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with open(f'json/fomc-impact/companies/{symbol}.json', 'w') as file:
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ujson.dump(data, file)
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# Function to fetch data from the API
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async def get_data(session, url):
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async with session.get(url) as response:
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data = await response.json()
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return data
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async def get_fomc_data():
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fomc_data = []
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start_date = datetime.now() - timedelta(days=365)
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end_date = datetime.now()
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async with aiohttp.ClientSession() as session:
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current_date = start_date
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while current_date < end_date:
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next_date = min(current_date + timedelta(days=10), end_date)
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start_str = current_date.strftime('%Y-%m-%d')
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end_str = next_date.strftime('%Y-%m-%d')
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url = f"https://financialmodelingprep.com/api/v3/economic_calendar?from={start_str}&to={end_str}&apikey={api_key}"
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data = await get_data(session, url)
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if data:
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# Filter for "FOMC Economic Projections" events
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fomc_events = [item for item in data if item.get('event') == "Fed Interest Rate Decision"]
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fomc_data.extend(fomc_events)
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# Move to the next 10-day period
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current_date = next_date
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filtered_data = [
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{
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'date': item['date'][0:10],
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'changePercentage': item['changePercentage'],
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'previous': item['previous'],
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'actual': item['actual'],
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'estimate': item['estimate']
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}
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for item in fomc_data
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]
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filtered_data = sorted(filtered_data, key=lambda x: x['date'])
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return filtered_data
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async def run():
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fomc_dates = await get_fomc_data() # Assumed to return the list of dictionaries as provided
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start_date = datetime.now() - timedelta(days=365)
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end_date = datetime.now()
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# Extracting the dates for filtering
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fomc_dates_list = [datetime.strptime(fomc['date'], '%Y-%m-%d').date() for fomc in fomc_dates]
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# Connect to SQLite databases
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stock_con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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stock_cursor = stock_con.cursor()
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stock_cursor.execute("PRAGMA journal_mode = wal")
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stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND marketCap >= 500E6")
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stock_symbols = [row[0] for row in stock_cursor.fetchall()]
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etf_cursor = etf_con.cursor()
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etf_cursor.execute("PRAGMA journal_mode = wal")
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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 = stock_symbols + etf_symbols
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for ticker in tqdm(total_symbols):
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try:
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query = query_template.format(ticker=ticker)
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connection = stock_con if ticker in stock_symbols else etf_con
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df_price = pd.read_sql_query(query, connection, params=(start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d')))
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if len(df_price) > 150 and len(fomc_dates) > 0:
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# Convert 'date' column in df_price to datetime.date for comparison
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df_price['date'] = pd.to_datetime(df_price['date']).dt.date
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# Filter out every fifth row, unless the date is in fomc_dates
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filtered_df = df_price[
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(df_price.index % 5 != 0) | (df_price['date'].isin(fomc_dates_list))
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]
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filtered_df['date'] = filtered_df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))
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# Prepare the result with filtered data and original fomc_dates
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fomc_data_unique = {}
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for fomc in fomc_dates:
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date = fomc['date']
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if date not in fomc_data_unique: # Check for duplicates
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fomc_data_unique[date] = {
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'date': date,
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'changePercentage': fomc['changePercentage'],
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'previous': fomc['previous'],
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'actual': fomc['actual'],
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'estimate': fomc['estimate']
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}
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# Convert the unique FOMC data back to a list
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res = {
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'fomcData': list(fomc_data_unique.values()), # Ensure unique dates
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'history': filtered_df.to_dict('records')
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}
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# Compute percentage changes for FOMC dates
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for i in range(len(res['fomcData'])):
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current_fomc_date = res['fomcData'][i]['date']
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current_price_row = filtered_df[filtered_df['date'] == current_fomc_date]
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if i == len(res['fomcData']) - 1:
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# This is the last FOMC date, so compare it to the last price in the dataframe
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last_price_row = filtered_df.iloc[-1]
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current_price = current_price_row['close'].values[0]
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next_price = last_price_row['close']
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else:
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next_fomc_date = res['fomcData'][i + 1]['date']
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next_price_row = filtered_df[filtered_df['date'] == next_fomc_date]
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if not current_price_row.empty and not next_price_row.empty:
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current_price = current_price_row['close'].values[0]
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next_price = next_price_row['close'].values[0]
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# Calculate the percentage change
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percentage_change = ((next_price - current_price) / current_price) * 100
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res['fomcData'][i]['changePercentage'] = round(percentage_change, 2) # Update with the new change percentage
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await save_json(ticker, res)
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except Exception as e:
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print(f"Error processing {ticker}: {e}")
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# Run the asyncio event loop
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loop = asyncio.get_event_loop()
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loop.run_until_complete(run())
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