437 lines
18 KiB
Python
437 lines
18 KiB
Python
import orjson
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import asyncio
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import aiohttp
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import aiofiles
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import sqlite3
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from datetime import datetime
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from ml_models.score_model import ScorePredictor
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import yfinance as yf
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from collections import defaultdict
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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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import re
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from itertools import combinations
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from ta.momentum import *
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from ta.trend import *
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from ta.volatility import *
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from ta.volume import *
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import gc
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#Enable automatic garbage collection
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gc.enable()
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async def save_json(symbol, data):
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with open(f"json/ai-score/companies/{symbol}.json", 'wb') as file:
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file.write(orjson.dumps(data))
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def trend_intensity(close, window=20):
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ma = close.rolling(window=window).mean()
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std = close.rolling(window=window).std()
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return ((close - ma) / std).abs().rolling(window=window).mean()
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def calculate_fdi(high, low, close, window=30):
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n1 = (np.log(high.rolling(window=window).max() - low.rolling(window=window).min()) -
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np.log(close.rolling(window=window).max() - close.rolling(window=window).min())) / np.log(2)
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return (2 - n1) * 100
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async def download_data(ticker, con, start_date, end_date):
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try:
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# Define paths to the statement files
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statements = [
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f"json/financial-statements/ratios/quarter/{ticker}.json",
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f"json/financial-statements/key-metrics/quarter/{ticker}.json",
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#f"json/financial-statements/cash-flow-statement/quarter/{ticker}.json",
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#f"json/financial-statements/income-statement/quarter/{ticker}.json",
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#f"json/financial-statements/balance-sheet-statement/quarter/{ticker}.json",
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f"json/financial-statements/income-statement-growth/quarter/{ticker}.json",
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f"json/financial-statements/balance-sheet-statement-growth/quarter/{ticker}.json",
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f"json/financial-statements/cash-flow-statement-growth/quarter/{ticker}.json",
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#f"json/financial-statements/owner-earnings/quarter/{ticker}.json",
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]
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# Helper function to load JSON data asynchronously
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async def load_json_from_file(path):
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async with aiofiles.open(path, 'r') as f:
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content = await f.read()
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return orjson.loads(content)
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# Helper function to filter data based on keys and year
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async def filter_data(data, ignore_keys, year_threshold=2000):
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return [{k: v for k, v in item.items() if k not in ignore_keys} for item in data if int(item["date"][:4]) >= year_threshold]
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# Define keys to ignore
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ignore_keys = ["symbol", "reportedCurrency", "calendarYear", "fillingDate", "acceptedDate", "period", "cik", "link", "finalLink","pbRatio","ptbRatio"]
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# Load and filter data for each statement type
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ratios = await load_json_from_file(statements[0])
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ratios = await filter_data(ratios, ignore_keys)
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#Threshold of enough datapoints needed!
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if len(ratios) < 50:
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return
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key_metrics = await load_json_from_file(statements[1])
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key_metrics = await filter_data(key_metrics, ignore_keys)
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#cashflow = await load_json_from_file(statements[2])
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#cashflow = await filter_data(cashflow, ignore_keys)
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#income = await load_json_from_file(statements[3])
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#income = await filter_data(income, ignore_keys)
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#balance = await load_json_from_file(statements[4])
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#balance = await filter_data(balance, ignore_keys)
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income_growth = await load_json_from_file(statements[2])
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income_growth = await filter_data(income_growth, ignore_keys)
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balance_growth = await load_json_from_file(statements[3])
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balance_growth = await filter_data(balance_growth, ignore_keys)
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cashflow_growth = await load_json_from_file(statements[4])
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cashflow_growth = await filter_data(cashflow_growth, ignore_keys)
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#owner_earnings = await load_json_from_file(statements[8])
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#owner_earnings = await filter_data(owner_earnings, ignore_keys)
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# Combine all the data
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combined_data = defaultdict(dict)
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# Merge the data based on 'date'
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for entries in zip(ratios, key_metrics,income_growth, balance_growth, cashflow_growth):
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for entry in entries:
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date = entry['date']
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for key, value in entry.items():
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if key not in combined_data[date]:
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combined_data[date][key] = value
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combined_data = list(combined_data.values())
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# Download historical stock data using yfinance
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df = yf.download(ticker, start=start_date, end=end_date, interval="1d").reset_index()
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df = df.rename(columns={'Adj Close': 'close', 'Date': 'date', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Volume': 'volume'})
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df['date'] = df['date'].dt.strftime('%Y-%m-%d')
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df['sma_50'] = df['close'].rolling(window=50).mean()
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df['sma_200'] = df['close'].rolling(window=200).mean()
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df['sma_crossover'] = ((df['sma_50'] > df['sma_200']) & (df['sma_50'].shift(1) <= df['sma_200'].shift(1))).astype(int)
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df['ema_50'] = EMAIndicator(close=df['close'], window=50).ema_indicator()
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df['ema_200'] = EMAIndicator(close=df['close'], window=200).ema_indicator()
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df['ema_crossover'] = ((df['ema_50'] > df['ema_200']) & (df['ema_50'].shift(1) <= df['ema_200'].shift(1))).astype(int)
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ichimoku = IchimokuIndicator(high=df['high'], low=df['low'])
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df['ichimoku_a'] = ichimoku.ichimoku_a()
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df['ichimoku_b'] = ichimoku.ichimoku_b()
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df['atr'] = AverageTrueRange(high=df['high'], low=df['low'], close=df['close']).average_true_range()
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bb = BollingerBands(close=df['close'])
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df['bb_width'] = (bb.bollinger_hband() - bb.bollinger_lband()) / df['close']
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df['volatility'] = df['close'].rolling(window=30).std()
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df['daily_return'] = df['close'].pct_change()
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df['cumulative_return'] = (1 + df['daily_return']).cumprod() - 1
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df['volume_change'] = df['volume'].pct_change()
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df['roc'] = df['close'].pct_change(periods=60)
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df['avg_volume'] = df['volume'].rolling(window=60).mean()
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df['drawdown'] = df['close'] / df['close'].rolling(window=252).max() - 1
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df['macd'] = macd(df['close'])
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df['macd_signal'] = macd_signal(df['close'])
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df['macd_hist'] = 2*macd_diff(df['close'])
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df['adx'] = adx(df['high'],df['low'],df['close'])
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df["adx_pos"] = adx_pos(df['high'],df['low'],df['close'])
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df["adx_neg"] = adx_neg(df['high'],df['low'],df['close'])
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df['cci'] = CCIIndicator(high=df['high'], low=df['low'], close=df['close']).cci()
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df['mfi'] = MFIIndicator(high=df['high'], low=df['low'], close=df['close'], volume=df['volume']).money_flow_index()
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df['nvi'] = NegativeVolumeIndexIndicator(close=df['close'], volume=df['volume']).negative_volume_index()
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df['obv'] = OnBalanceVolumeIndicator(close=df['close'], volume=df['volume']).on_balance_volume()
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df['vpt'] = VolumePriceTrendIndicator(close=df['close'], volume=df['volume']).volume_price_trend()
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df['rsi'] = rsi(df["close"], window=60)
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df['rolling_rsi'] = df['rsi'].rolling(window=10).mean()
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df['stoch_rsi'] = stochrsi_k(df['close'], window=60, smooth1=3, smooth2=3)
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df['rolling_stoch_rsi'] = df['stoch_rsi'].rolling(window=10).mean()
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df['adi'] = acc_dist_index(high=df['high'],low=df['low'],close=df['close'],volume=df['volume'])
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df['cmf'] = chaikin_money_flow(high=df['high'],low=df['low'],close=df['close'],volume=df['volume'], window=20)
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df['emv'] = ease_of_movement(high=df['high'],low=df['low'],volume=df['volume'], window=20)
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df['fi'] = force_index(close=df['close'], volume=df['volume'], window= 13)
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df['williams'] = WilliamsRIndicator(high=df['high'], low=df['low'], close=df['close']).williams_r()
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df['kama'] = KAMAIndicator(close=df['close']).kama()
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df['stoch'] = stoch(df['high'], df['low'], df['close'], window=30)
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df['rocr'] = df['close'] / df['close'].shift(30) - 1 # Rate of Change Ratio (ROCR)
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df['ppo'] = (df['ema_50'] - df['ema_200']) / df['ema_50'] * 100
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df['vwap'] = (df['volume'] * (df['high'] + df['low'] + df['close']) / 3).cumsum() / df['volume'].cumsum()
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df['volatility_ratio'] = df['close'].rolling(window=30).std() / df['close'].rolling(window=60).std()
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df['fdi'] = calculate_fdi(df['high'], df['low'], df['close'])
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df['tii'] = trend_intensity(df['close'])
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ta_indicators = [
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'rsi', 'macd', 'macd_signal', 'macd_hist', 'adx', 'adx_pos', 'adx_neg',
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'cci', 'mfi', 'nvi', 'obv', 'vpt', 'stoch_rsi','bb_width',
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'adi', 'cmf', 'emv', 'fi', 'williams', 'stoch','sma_crossover',
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'volatility','daily_return','cumulative_return', 'roc','avg_volume',
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'rolling_rsi','rolling_stoch_rsi', 'ema_crossover','ichimoku_a','ichimoku_b',
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'atr','kama','rocr','ppo','volatility_ratio','vwap','tii','fdi','drawdown',
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'volume_change'
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]
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# Match each combined data entry with the closest available stock price in df
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for item in combined_data:
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target_date = item['date']
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counter = 0
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max_attempts = 10
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# Look for the closest matching date in the stock data
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while target_date not in df['date'].values and counter < max_attempts:
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target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
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counter += 1
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# If max attempts are reached and no matching date is found, skip the entry
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if counter == max_attempts:
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continue
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# Find the close price for the matching date
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close_price = round(df[df['date'] == target_date]['close'].values[0], 2)
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item['price'] = close_price
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# Dynamically add all indicator values to the combined_data entry
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for indicator in ta_indicators:
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indicator_value = df[df['date'] == target_date][indicator].values[0]
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item[indicator] = indicator_value # Add the indicator value to the combined_data entry
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# Sort the combined data by date
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combined_data = sorted(combined_data, key=lambda x: x['date'])
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# Convert combined data into a DataFrame
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df_combined = pd.DataFrame(combined_data).dropna()
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'''
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key_elements = [
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'revenue',
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'costOfRevenue',
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'grossProfit',
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'netIncome',
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'operatingIncome',
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'operatingExpenses',
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'researchAndDevelopmentExpenses',
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'ebitda',
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'freeCashFlow',
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'incomeBeforeTax',
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'incomeTaxExpense',
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'debtRepayment',
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'dividendsPaid',
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'depreciationAndAmortization',
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'netCashUsedProvidedByFinancingActivities',
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'changeInWorkingCapital',
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'stockBasedCompensation',
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'deferredIncomeTax',
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'commonStockRepurchased',
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'operatingCashFlow',
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'capitalExpenditure',
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'accountsReceivables',
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'purchasesOfInvestments',
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'cashAndCashEquivalents',
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'shortTermInvestments',
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'cashAndShortTermInvestments',
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'longTermInvestments',
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'otherCurrentLiabilities',
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'totalCurrentLiabilities',
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'longTermDebt',
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'totalDebt',
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'netDebt',
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'commonStock',
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'totalEquity',
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'totalLiabilitiesAndStockholdersEquity',
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'totalStockholdersEquity',
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'totalInvestments',
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'taxAssets',
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'totalAssets',
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'inventory',
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'propertyPlantEquipmentNet',
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'ownersEarnings',
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]
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# Compute ratios for all combinations of key elements
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new_columns = {}
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# Loop over combinations of column pairs
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for num, denom in combinations(key_elements, 2):
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# Compute ratio and reverse ratio
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ratio = df_combined[num] / df_combined[denom]
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reverse_ratio = df_combined[denom] / df_combined[num]
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# Define column names for both ratios
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column_name = f'{num}_to_{denom}'
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reverse_column_name = f'{denom}_to_{num}'
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# Store the new columns in the dictionary, replacing invalid values with 0
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new_columns[column_name] = np.nan_to_num(ratio, nan=0, posinf=0, neginf=0)
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new_columns[reverse_column_name] = np.nan_to_num(reverse_ratio, nan=0, posinf=0, neginf=0)
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# Add all new columns to the original DataFrame at once
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df_combined = pd.concat([df_combined, pd.DataFrame(new_columns)], axis=1)
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'''
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# To defragment the DataFrame, make a copy
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df_combined = df_combined.copy()
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# Create 'Target' column based on price change
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df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
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# Return a copy of the combined DataFrame
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df_combined = df_combined.dropna()
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df_combined = df_combined.where(~df_combined.isin([np.inf, -np.inf]), 0)
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df_copy = df_combined.copy()
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return df_copy
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except Exception as e:
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print(e)
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pass
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async def chunked_gather(tickers, con, start_date, end_date, chunk_size=10):
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# Helper function to divide the tickers into chunks
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def chunks(lst, size):
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for i in range(0, len(lst), size):
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yield lst[i:i+size]
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results = []
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for chunk in chunks(tickers, chunk_size):
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# Create tasks for each chunk
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tasks = [download_data(ticker, con, start_date, end_date) for ticker in chunk]
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# Await the results for the current chunk
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chunk_results = await asyncio.gather(*tasks)
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# Accumulate the results
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results.extend(chunk_results)
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return results
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async def warm_start_training(tickers, con):
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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df_train = pd.DataFrame()
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df_test = pd.DataFrame()
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test_size = 0.2
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dfs = await chunked_gather(tickers, con, start_date, end_date, chunk_size=10)
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train_list = []
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test_list = []
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for df in dfs:
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try:
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split_size = int(len(df) * (1 - test_size))
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train_data = df.iloc[:split_size]
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test_data = df.iloc[split_size:]
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# Append to the lists
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train_list.append(train_data)
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test_list.append(test_data)
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except:
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pass
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# Concatenate all at once outside the loop
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df_train = pd.concat(train_list, ignore_index=True)
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df_test = pd.concat(test_list, ignore_index=True)
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print('======Warm Start Train Set Datapoints======')
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df_train = df_train.sample(frac=1).reset_index(drop=True) #df_train.reset_index(drop=True)
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print(len(df_train))
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predictor = ScorePredictor()
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selected_features = [col for col in df_train if col not in ['price', 'date', 'Target']]
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predictor.warm_start_training(df_train[selected_features], df_train['Target'])
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predictor.evaluate_model(df_test[selected_features], df_test['Target'])
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return predictor
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async def fine_tune_and_evaluate(ticker, con, start_date, end_date):
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try:
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df = await download_data(ticker, con, start_date, end_date)
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if df is None or len(df) == 0:
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print(f"No data available for {ticker}")
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return
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test_size = 0.2
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split_size = int(len(df) * (1-test_size))
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train_data = df.iloc[:split_size]
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test_data = df.iloc[split_size:]
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selected_features = [col for col in df.columns if col not in ['date','price','Target']]
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# Fine-tune the model
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predictor = ScorePredictor()
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predictor.fine_tune_model(train_data[selected_features], train_data['Target'])
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print(f"Evaluating fine-tuned model for {ticker}")
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data = predictor.evaluate_model(test_data[selected_features], test_data['Target'])
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if len(data) != 0:
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if data['precision'] >= 60 and data['accuracy'] >= 60 and data['accuracy'] < 100 and data['precision'] < 100:
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res = {'score': data['score']}
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await save_json(ticker, res)
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print(f"Saved results for {ticker}")
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gc.collect()
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except Exception as e:
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print(f"Error processing {ticker}: {e}")
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finally:
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# Ensure any remaining cleanup if necessary
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if 'predictor' in locals():
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del predictor # Explicitly delete the predictor to aid garbage collection
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async def run():
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train_mode = False # Set this to False for fine-tuning and evaluation
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con = sqlite3.connect('stocks.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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if train_mode:
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# Warm start training
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E9 AND symbol NOT LIKE '%.%' AND symbol NOT LIKE '%-%'")
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warm_start_symbols = [row[0] for row in cursor.fetchall()]
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print('Warm Start Training for:', warm_start_symbols)
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predictor = await warm_start_training(warm_start_symbols, con)
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else:
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# Fine-tuning and evaluation for all stocks
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 1E9 AND symbol NOT LIKE '%.%'")
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stock_symbols = ['GME'] #[row[0] for row in cursor.fetchall()]
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print(f"Total tickers for fine-tuning: {len(stock_symbols)}")
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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tasks = []
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for ticker in tqdm(stock_symbols):
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await fine_tune_and_evaluate(ticker, con, start_date, end_date)
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#await asyncio.gather(*tasks)
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con.close()
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if __name__ == "__main__":
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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(f"Main execution error: {e}")
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