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update fundamental ml model

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This commit is contained in:
MuslemRahimi 2024-09-28 14:50:38 +02:00
parent ed11719628
commit ac81bd613a
4 changed files with 142 additions and 231 deletions
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195
app/cron_fundamental_predictor.py
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@ -1,6 +1,7 @@
import ujson
import orjson
import asyncio
import aiohttp
import aiofiles
import sqlite3
from datetime import datetime
from ml_models.fundamental_predictor import FundamentalPredictor
@ -10,103 +11,114 @@ import pandas as pd
from tqdm import tqdm
import concurrent.futures
import re
import subprocess
async def save_json(symbol, data):
with open(f"json/fundamental-predictor-analysis/{symbol}.json", 'w') as file:
ujson.dump(data, file)
orjson.dump(data, file)
async def download_data(ticker, con, start_date, end_date):
try:
query_template = """
SELECT
income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios
FROM
stocks
WHERE
symbol = ?
"""
# Define paths to the statement files
statements = [
f"json/financial-statements/ratios/quarter/{ticker}.json",
f"json/financial-statements/cash-flow-statement/quarter/{ticker}.json",
f"json/financial-statements/income-statement/quarter/{ticker}.json",
f"json/financial-statements/balance-sheet-statement/quarter/{ticker}.json",
f"json/financial-statements/income-statement-growth/quarter/{ticker}.json",
f"json/financial-statements/balance-sheet-statement-growth/quarter/{ticker}.json",
f"json/financial-statements/cash-flow-statement-growth/quarter/{ticker}.json"
]
query_df = pd.read_sql_query(query_template, con, params=(ticker,))
# Helper function to load JSON data asynchronously
async def load_json_from_file(path):
async with aiofiles.open(path, 'r') as f:
content = await f.read()
return orjson.loads(content)
income = ujson.loads(query_df['income'].iloc[0])
#Only consider company with at least 10 year worth of data
if len(income) < 40:
raise ValueError("Income data length is too small.")
# Helper function to filter data based on keys and year
async def filter_data(data, ignore_keys, year_threshold=2000):
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]
income = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in income if int(item["date"][:4]) >= 2000]
income_growth = ujson.loads(query_df['income_growth'].iloc[0])
income_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in income_growth if int(item["date"][:4]) >= 2000]
balance = ujson.loads(query_df['balance'].iloc[0])
balance = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in balance if int(item["date"][:4]) >= 2000]
balance_growth = ujson.loads(query_df['balance_growth'].iloc[0])
balance_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in balance_growth if int(item["date"][:4]) >= 2000]
# Define keys to ignore
ignore_keys = ["symbol", "reportedCurrency", "calendarYear", "fillingDate", "acceptedDate", "period", "cik", "link", "finalLink"]
cashflow = ujson.loads(query_df['cashflow'].iloc[0])
cashflow = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in cashflow if int(item["date"][:4]) >= 2000]
cashflow_growth = ujson.loads(query_df['cashflow_growth'].iloc[0])
cashflow_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in cashflow_growth if int(item["date"][:4]) >= 2000]
# Load and filter data for each statement type
income = await load_json_from_file(statements[2])
income = await filter_data(income, ignore_keys)
income_growth = await load_json_from_file(statements[4])
income_growth = await filter_data(income_growth, ignore_keys)
ratios = ujson.loads(query_df['ratios'].iloc[0])
ratios = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in ratios if int(item["date"][:4]) >= 2000]
balance = await load_json_from_file(statements[3])
balance = await filter_data(balance, ignore_keys)
balance_growth = await load_json_from_file(statements[5])
balance_growth = await filter_data(balance_growth, ignore_keys)
cashflow = await load_json_from_file(statements[1])
cashflow = await filter_data(cashflow, ignore_keys)
cashflow_growth = await load_json_from_file(statements[6])
cashflow_growth = await filter_data(cashflow_growth, ignore_keys)
ratios = await load_json_from_file(statements[0])
ratios = await filter_data(ratios, ignore_keys)
# Combine all the data
combined_data = defaultdict(dict)
# Iterate over all lists simultaneously
# Merge the data based on 'date'
for entries in zip(income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios):
# Iterate over each entry in the current set of entries
for entry in entries:
date = entry['date']
# Merge entry data into combined_data, skipping duplicate keys
for key, value in entry.items():
if key not in combined_data[date]:
combined_data[date][key] = value
combined_data = list(combined_data.values())
# Download historical stock data using yfinance
df = yf.download(ticker, start=start_date, end=end_date, interval="1d").reset_index()
df = df.rename(columns={'Adj Close': 'close', 'Date': 'date'})
#print(df[['date','close']])
df['date'] = df['date'].dt.strftime('%Y-%m-%d')
# Match each combined data entry with the closest available stock price in df
for item in combined_data:
# Find close price for '2023-09-30' or the closest available date prior to it
target_date = item['date']
counter = 0
max_attempts = 10
while target_date not in df['date'].values and counter < max_attempts:
# If the target date doesn't exist, move one day back
target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
counter += 1
if counter == max_attempts:
break
# Get the close price for the found or closest date
close_price = round(df[df['date'] == target_date]['close'].values[0],2)
# Look for the closest matching date in the stock data
while target_date not in df['date'].values and counter < max_attempts:
target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
counter += 1
# If max attempts are reached and no matching date is found, skip the entry
if counter == max_attempts:
continue
# Find the close price for the matching date
close_price = round(df[df['date'] == target_date]['close'].values[0], 2)
item['price'] = close_price
#print(f"Close price for {target_date}: {close_price}")
# Sort the combined data by date
combined_data = sorted(combined_data, key=lambda x: x['date'])
df_income = pd.DataFrame(combined_data).dropna()
# Convert combined data into a DataFrame
df_combined = pd.DataFrame(combined_data).dropna()
df_income['Target'] = ((df_income['price'].shift(-1) - df_income['price']) / df_income['price'] > 0).astype(int)
# Create 'Target' column based on price change
df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
df_copy = df_income.copy()
#print(df_copy)
# Return a copy of the combined DataFrame
df_copy = df_combined.copy()
return df_copy
except Exception as e:
print(e)
except:
pass
async def process_symbol(ticker, con, start_date, end_date):
@ -114,7 +126,7 @@ async def process_symbol(ticker, con, start_date, end_date):
test_size = 0.4
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
predictor = FundamentalPredictor(path="ml_models/weights")
predictor = FundamentalPredictor()
df = await download_data(ticker, con, start_date, end_date)
split_size = int(len(df) * (1-test_size))
test_data = df.iloc[split_size:]
@ -135,11 +147,75 @@ async def process_symbol(ticker, con, start_date, end_date):
except Exception as e:
print(e)
#Train mode
async def train_process(tickers, con):
tickers = list(set(tickers))
df_train = pd.DataFrame()
df_test = pd.DataFrame()
test_size = 0.4
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
predictor = FundamentalPredictor()
df_train = pd.DataFrame()
df_test = pd.DataFrame()
tasks = [download_data(ticker, con, start_date, end_date) for ticker in tickers]
dfs = await asyncio.gather(*tasks)
for df in dfs:
try:
split_size = int(len(df) * (1-test_size))
train_data = df.iloc[:split_size]
test_data = df.iloc[split_size:]
df_train = pd.concat([df_train, train_data], ignore_index=True)
df_test = pd.concat([df_test, test_data], ignore_index=True)
except:
pass
best_features = [col for col in df_train.columns if col not in ['date','price','Target']]
df_train = df_train.sample(frac=1).reset_index(drop=True)
print('======Train Set Datapoints======')
print(len(df_train))
#selected_features = predictor.feature_selection(df_train[best_features], df_train['Target'],k=10)
#print(selected_features)
#selected_features = [col for col in df_train if col not in ['price','date','Target']]
selected_features = ['shortTermCoverageRatios','netProfitMargin','debtRepayment','totalDebt','interestIncome','researchAndDevelopmentExpenses','priceEarningsToGrowthRatio','priceCashFlowRatio','cashPerShare','debtRatio','growthRevenue','revenue','growthNetIncome','ebitda','priceEarningsRatio','priceToBookRatio','epsdiluted','priceToSalesRatio','growthOtherCurrentLiabilities', 'receivablesTurnover', 'totalLiabilitiesAndStockholdersEquity', 'totalLiabilitiesAndTotalEquity', 'totalAssets', 'growthOtherCurrentAssets', 'retainedEarnings', 'totalEquity']
predictor.train_model(df_train[selected_features], df_train['Target'])
predictor.evaluate_model(df_test[selected_features], df_test['Target'])
async def test_process(con):
test_size = 0.4
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
predictor = FundamentalPredictor()
df = await download_data('GME', con, start_date, end_date)
split_size = int(len(df) * (1-test_size))
test_data = df.iloc[split_size:]
#selected_features = [col for col in test_data if col not in ['price','date','Target']]
selected_features = ['shortTermCoverageRatios','netProfitMargin','debtRepayment','totalDebt','interestIncome','researchAndDevelopmentExpenses','priceEarningsToGrowthRatio','priceCashFlowRatio','cashPerShare','debtRatio','growthRevenue','revenue','growthNetIncome','ebitda','priceEarningsRatio','priceToBookRatio','epsdiluted','priceToSalesRatio','growthOtherCurrentLiabilities', 'receivablesTurnover', 'totalLiabilitiesAndStockholdersEquity', 'totalLiabilitiesAndTotalEquity', 'totalAssets', 'growthOtherCurrentAssets', 'retainedEarnings', 'totalEquity']
predictor.evaluate_model(test_data[selected_features], test_data['Target'])
async def run():
#Train first model
con = sqlite3.connect('stocks.db')
cursor = con.cursor()
cursor.execute("PRAGMA journal_mode = wal")
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E9")
stock_symbols = [row[0] for row in cursor.fetchall()]
print('Number of Stocks')
print(len(stock_symbols))
await train_process(stock_symbols, con)
#Prediction Steps for all stock symbols
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 1E9")
stock_symbols = [row[0] for row in cursor.fetchall()]
@ -160,6 +236,7 @@ async def run():
await asyncio.gather(*tasks)
con.close()
try:
asyncio.run(run())
except Exception as e:

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app/ml_models/__pycache__/fundamental_predictor.cpython-310.pyc
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178
app/ml_models/fundamental_predictor.py
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@ -20,114 +20,16 @@ from tqdm import tqdm
from collections import defaultdict
import asyncio
import aiohttp
import aiofiles
import pickle
import time
import sqlite3
import ujson
#Based on the paper: https://arxiv.org/pdf/1603.00751
async def download_data(ticker, con, start_date, end_date):
try:
query_template = """
SELECT
income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios
FROM
stocks
WHERE
symbol = ?
"""
query_df = pd.read_sql_query(query_template, con, params=(ticker,))
income = ujson.loads(query_df['income'].iloc[0])
#Only consider company with at least 10 year worth of data
'''
if len(income) < 40:
raise ValueError("Income data length is too small.")
'''
income = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in income if int(item["date"][:4]) >= 2000]
income_growth = ujson.loads(query_df['income_growth'].iloc[0])
income_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in income_growth if int(item["date"][:4]) >= 2000]
balance = ujson.loads(query_df['balance'].iloc[0])
balance = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in balance if int(item["date"][:4]) >= 2000]
balance_growth = ujson.loads(query_df['balance_growth'].iloc[0])
balance_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in balance_growth if int(item["date"][:4]) >= 2000]
cashflow = ujson.loads(query_df['cashflow'].iloc[0])
cashflow = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in cashflow if int(item["date"][:4]) >= 2000]
cashflow_growth = ujson.loads(query_df['cashflow_growth'].iloc[0])
cashflow_growth = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in cashflow_growth if int(item["date"][:4]) >= 2000]
ratios = ujson.loads(query_df['ratios'].iloc[0])
ratios = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in ratios if int(item["date"][:4]) >= 2000]
combined_data = defaultdict(dict)
# Iterate over all lists simultaneously
for entries in zip(income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios):
# Iterate over each entry in the current set of entries
for entry in entries:
date = entry['date']
# Merge entry data into combined_data, skipping duplicate keys
for key, value in entry.items():
if key not in combined_data[date]:
combined_data[date][key] = value
combined_data = list(combined_data.values())
df = yf.download(ticker, start=start_date, end=end_date, interval="1d").reset_index()
df = df.rename(columns={'Adj Close': 'close', 'Date': 'date'})
#print(df[['date','close']])
df['date'] = df['date'].dt.strftime('%Y-%m-%d')
for item in combined_data:
# Find close price for '2023-09-30' or the closest available date prior to it
target_date = item['date']
counter = 0
max_attempts = 10
while target_date not in df['date'].values and counter < max_attempts:
# If the target date doesn't exist, move one day back
target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
counter += 1
if counter == max_attempts:
break
# Get the close price for the found or closest date
close_price = round(df[df['date'] == target_date]['close'].values[0],2)
item['price'] = close_price
#print(f"Close price for {target_date}: {close_price}")
combined_data = sorted(combined_data, key=lambda x: x['date'])
df_combined = pd.DataFrame(combined_data).dropna()
df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
df_copy = df_combined.copy()
return df_copy
except Exception as e:
print(e)
class FundamentalPredictor:
def __init__(self, path='weights'):
def __init__(self):
self.model = self.build_model() #RandomForestClassifier(n_estimators=1000, max_depth = 20, min_samples_split=10, random_state=42, n_jobs=10)
self.scaler = MinMaxScaler()
self.path = path
def build_model(self):
clear_session()
@ -183,18 +85,18 @@ class FundamentalPredictor:
X_train = self.preprocess_data(X_train)
X_train = self.reshape_for_lstm(X_train)
checkpoint = ModelCheckpoint(f'{self.path}/fundamental_weights/weights.keras', save_best_only=True, monitor='val_loss', mode='min')
checkpoint = ModelCheckpoint(f'ml_models/fundamental_weights/weights.keras', save_best_only=True, monitor='val_loss', mode='min')
early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
self.model.fit(X_train, y_train, epochs=250, batch_size=32, validation_split=0.2, callbacks=[checkpoint, early_stopping])
self.model.save(f'{self.path}/fundamental_weights/weights.keras')
self.model.save(f'ml_models/fundamental_weights/weights.keras')
def evaluate_model(self, X_test, y_test):
X_test = self.preprocess_data(X_test)
X_test = self.reshape_for_lstm(X_test)
self.model = self.build_model()
self.model = load_model(f'{self.path}/fundamental_weights/weights.keras')
self.model = load_model(f'ml_models/fundamental_weights/weights.keras')
test_predictions = self.model.predict(X_test).flatten()
@ -231,72 +133,4 @@ class FundamentalPredictor:
# Sort features by variance and select top k features
sorted_features = sorted(variances, key=variances.get, reverse=True)[:k]
return sorted_features
'''
#Train mode
async def train_process(tickers, con):
tickers = list(set(tickers))
df_train = pd.DataFrame()
df_test = pd.DataFrame()
test_size = 0.4
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
predictor = FundamentalPredictor()
df_train = pd.DataFrame()
df_test = pd.DataFrame()
tasks = [download_data(ticker, con, start_date, end_date) for ticker in tickers]
dfs = await asyncio.gather(*tasks)
for df in dfs:
try:
split_size = int(len(df) * (1-test_size))
train_data = df.iloc[:split_size]
test_data = df.iloc[split_size:]
df_train = pd.concat([df_train, train_data], ignore_index=True)
df_test = pd.concat([df_test, test_data], ignore_index=True)
except:
pass
best_features = [col for col in df_train.columns if col not in ['date','price','Target']]
df_train = df_train.sample(frac=1).reset_index(drop=True)
print('======Train Set Datapoints======')
print(len(df_train))
#selected_features = predictor.feature_selection(df_train[best_features], df_train['Target'],k=10)
#print(selected_features)
#selected_features = [col for col in df_train if col not in ['price','date','Target']]
selected_features = ['shortTermCoverageRatios','netProfitMargin','debtRepayment','totalDebt','interestIncome','researchAndDevelopmentExpenses','priceEarningsToGrowthRatio','priceCashFlowRatio','cashPerShare','debtRatio','growthRevenue','revenue','growthNetIncome','ebitda','priceEarningsRatio','priceToBookRatio','epsdiluted','priceToSalesRatio','growthOtherCurrentLiabilities', 'receivablesTurnover', 'totalLiabilitiesAndStockholdersEquity', 'totalLiabilitiesAndTotalEquity', 'totalAssets', 'growthOtherCurrentAssets', 'retainedEarnings', 'totalEquity']
predictor.train_model(df_train[selected_features], df_train['Target'])
predictor.evaluate_model(df_test[selected_features], df_test['Target'])
async def test_process(con):
test_size = 0.4
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
predictor = FundamentalPredictor()
df = await download_data('GME', con, start_date, end_date)
split_size = int(len(df) * (1-test_size))
test_data = df.iloc[split_size:]
#selected_features = [col for col in test_data if col not in ['price','date','Target']]
selected_features = ['shortTermCoverageRatios','netProfitMargin','debtRepayment','totalDebt','interestIncome','researchAndDevelopmentExpenses','priceEarningsToGrowthRatio','priceCashFlowRatio','cashPerShare','debtRatio','growthRevenue','revenue','growthNetIncome','ebitda','priceEarningsRatio','priceToBookRatio','epsdiluted','priceToSalesRatio','growthOtherCurrentLiabilities', 'receivablesTurnover', 'totalLiabilitiesAndStockholdersEquity', 'totalLiabilitiesAndTotalEquity', 'totalAssets', 'growthOtherCurrentAssets', 'retainedEarnings', 'totalEquity']
predictor.evaluate_model(test_data[selected_features], test_data['Target'])
async def main():
con = sqlite3.connect('../stocks.db')
cursor = con.cursor()
cursor.execute("PRAGMA journal_mode = wal")
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E9")
stock_symbols = [row[0] for row in cursor.fetchall()]
print('Number of Stocks')
print(len(stock_symbols))
await train_process(stock_symbols, con)
await test_process(con)
con.close()
# Run the main function
#asyncio.run(main())
'''

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