114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
import ujson
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import asyncio
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import aiohttp
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import sqlite3
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from datetime import datetime
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from ml_models.prophet_model import PricePredictor
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import yfinance as yf
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import pandas as pd
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from tqdm import tqdm
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import concurrent.futures
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def convert_symbols(symbol_list):
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"""
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Converts the symbols in the given list from 'BTCUSD' and 'USDTUSD' format to 'BTC-USD' and 'USDT-USD' format.
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Args:
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symbol_list (list): A list of strings representing the symbols to be converted.
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Returns:
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list: A new list with the symbols converted to the desired format.
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"""
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converted_symbols = []
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for symbol in symbol_list:
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# Determine the base and quote currencies
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base_currency = symbol[:-3]
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quote_currency = symbol[-3:]
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# Construct the new symbol in the desired format
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new_symbol = f"{base_currency}-{quote_currency}"
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converted_symbols.append(new_symbol)
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return converted_symbols
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async def save_json(symbol, data):
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with open(f"json/price-analysis/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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async def download_data(ticker, start_date, end_date):
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try:
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df = yf.download(ticker, start=start_date, end=end_date, interval="1d")
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df = df.reset_index()
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df = df[['Date', 'Adj Close']]
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df = df.rename(columns={"Date": "ds", "Adj Close": "y"})
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if len(df) > 252*2: #At least 2 years of history is necessary
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q_high= df["y"].quantile(0.99)
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q_low = df["y"].quantile(0.05)
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df = df[(df["y"] > q_low)]
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df = df[(df["y"] < q_high)]
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#df['y'] = df['y'].rolling(window=10).mean()
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#df = df.dropna()
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return df
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except Exception as e:
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print(e)
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async def process_symbol(ticker, start_date, end_date, crypto_symbols):
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try:
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df = await download_data(ticker, start_date, end_date)
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data = PricePredictor().run(df)
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if ticker in crypto_symbols:
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ticker = ticker.replace('-','') #convert back from BTC-USD to BTCUSD
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await save_json(ticker, data)
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except Exception as e:
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print(e)
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async def run():
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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crypto_con = sqlite3.connect('crypto.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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#cursor.execute("SELECT DISTINCT symbol FROM stocks")
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#cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap > 10E9 AND symbol NOT LIKE '%.%'")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap > 1E9")
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stock_symbols = [row[0] for row in 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 WHERE totalAssets > 5E9")
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etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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crypto_cursor = crypto_con.cursor()
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crypto_cursor.execute("PRAGMA journal_mode = wal")
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crypto_cursor.execute("SELECT DISTINCT symbol FROM cryptos")
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crypto_symbols = [row[0] for row in crypto_cursor.fetchall()]
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crypto_symbols = convert_symbols(crypto_symbols) #Convert BTCUSD to BTC-USD for yfinance
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con.close()
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etf_con.close()
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crypto_con.close()
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total_symbols = stock_symbols + etf_symbols + crypto_symbols
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print(f"Total tickers: {len(total_symbols)}")
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start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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chunk_size = len(total_symbols) // 70 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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for chunk in chunks:
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tasks = []
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for ticker in tqdm(chunk):
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tasks.append(process_symbol(ticker, start_date, end_date, crypto_symbols))
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await asyncio.gather(*tasks)
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try:
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asyncio.run(run())
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except Exception as e:
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print(e)
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