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add ai score feature

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This commit is contained in:
MuslemRahimi 2024-09-30 23:46:23 +02:00
parent 85f5dbe075
commit 498edf7a80
4 changed files with 39 additions and 26 deletions
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52
app/cron_ai_score.py
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@ -49,14 +49,15 @@ async def download_data(ticker, con, start_date, end_date):
# 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",
f"json/financial-statements/key-metrics/quarter/{ticker}.json",
f"json/financial-statements/owner-earnings/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",
#f"json/financial-statements/key-metrics/quarter/{ticker}.json",
#f"json/financial-statements/owner-earnings/quarter/{ticker}.json",
]
# Helper function to load JSON data asynchronously
@ -76,7 +77,11 @@ async def download_data(ticker, con, start_date, end_date):
ratios = await load_json_from_file(statements[0])
ratios = await filter_data(ratios, ignore_keys)
key_metrics = await load_json_from_file(statements[1])
key_metrics = await filter_data(key_metrics, ignore_keys)
'''
cashflow = await load_json_from_file(statements[1])
cashflow = await filter_data(cashflow, ignore_keys)
@ -96,18 +101,15 @@ async def download_data(ticker, con, start_date, end_date):
cashflow_growth = await load_json_from_file(statements[6])
cashflow_growth = await filter_data(cashflow_growth, ignore_keys)
key_metrics = await load_json_from_file(statements[7])
key_metrics = await filter_data(key_metrics, ignore_keys)
owner_earnings = await load_json_from_file(statements[8])
owner_earnings = await load_json_from_file(statements[7])
owner_earnings = await filter_data(owner_earnings, ignore_keys)
'''
# Combine all the data
combined_data = defaultdict(dict)
# Merge the data based on 'date'
for entries in zip(income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios, key_metrics, owner_earnings):
for entries in zip(ratios, key_metrics):
for entry in entries:
date = entry['date']
for key, value in entry.items():
@ -189,7 +191,8 @@ async def download_data(ticker, con, start_date, end_date):
'adi', 'cmf', 'emv', 'fi', 'williams', 'stoch','sma_crossover',
'volatility','daily_return','cumulative_return', 'roc','avg_volume_30d',
'rolling_rsi','rolling_stoch_rsi', 'ema_crossover','ichimoku_a','ichimoku_b',
'atr','kama','rocr','ppo','volatility_ratio','vwap','tii','fdi'
'atr','kama','rocr','ppo','volatility_ratio','vwap','tii','fdi','drawdown',
'volume_change'
]
# Match each combined data entry with the closest available stock price in df
@ -222,6 +225,8 @@ async def download_data(ticker, con, start_date, end_date):
combined_data = sorted(combined_data, key=lambda x: x['date'])
# Convert combined data into a DataFrame
df_combined = pd.DataFrame(combined_data).dropna()
'''
key_elements = [
'revenue',
'costOfRevenue',
@ -287,6 +292,7 @@ async def download_data(ticker, con, start_date, end_date):
# Add all new columns to the original DataFrame at once
df_combined = pd.concat([df_combined, pd.DataFrame(new_columns)], axis=1)
'''
# To defragment the DataFrame, make a copy
df_combined = df_combined.copy()
@ -316,10 +322,10 @@ async def process_symbol(ticker, con, start_date, end_date):
df = await download_data(ticker, 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 df.columns if col not in ['date','price','Target']]
best_features = ['williams', 'stoch', 'fdi', 'revenue_to_cashAndCashEquivalents', 'revenue_to_cashAndShortTermInvestments', 'costOfRevenue_to_cashAndCashEquivalents', 'costOfRevenue_to_cashAndShortTermInvestments', 'ebitda_to_cashAndShortTermInvestments', 'incomeTaxExpense_to_cashAndCashEquivalents', 'incomeTaxExpense_to_cashAndShortTermInvestments', 'capitalExpenditure_to_cashAndCashEquivalents', 'capitalExpenditure_to_cashAndShortTermInvestments', 'totalCurrentLiabilities_to_cashAndShortTermInvestments', 'netDebt_to_cashAndShortTermInvestments', 'inventory_to_cashAndShortTermInvestments']
selected_features = [col for col in df.columns if col not in ['date','price','Target']]
print(f"For the Ticker: {ticker}")
data = predictor.evaluate_model(test_data[best_features], test_data['Target'])
data = predictor.evaluate_model(test_data[selected_features], test_data['Target'])
if len(data) != 0:
if data['precision'] >= 50 and data['accuracy'] >= 50:
@ -391,10 +397,10 @@ async def train_process(tickers, con):
predictor = ScorePredictor()
#print(selected_features)
selected_features = [col for col in df_train if col not in ['price','date','Target']]
best_features = predictor.feature_selection(df_train[selected_features], df_train['Target'],k=15)
print(best_features)
predictor.train_model(df_train[best_features], df_train['Target'])
predictor.evaluate_model(df_test[best_features], df_test['Target'])
#best_features = predictor.feature_selection(df_train[selected_features], df_train['Target'],k=15)
#print(best_features)
predictor.train_model(df_train[selected_features], df_train['Target'])
predictor.evaluate_model(df_test[selected_features], df_test['Target'])
async def run():
@ -406,7 +412,7 @@ async def run():
if train_mode:
#Train first model
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E9 AND symbol NOT LIKE '%.%'")
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E9 AND symbol NOT LIKE '%.%'")
stock_symbols = [row[0] for row in cursor.fetchall()]
print('Number of Stocks')
print(len(stock_symbols))
@ -423,7 +429,7 @@ async def run():
start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
chunk_size = len(total_symbols)// 100 # Divide the list into N chunks
chunk_size = len(total_symbols) // 100 # Divide the list into N chunks
chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
for chunk in chunks:
tasks = []

7
app/cron_stockdeck.py
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@ -50,6 +50,12 @@ async def get_data(ticker):
except:
company_quote = {}
try:
with open(f"json/ai-score/companies/{ticker}.json", 'r') as file:
score = ujson.load(file)['score']
except:
score = None
try:
with open(f"json/forward-pe/{ticker}.json", 'r') as file:
forward_pe = ujson.load(file)['forwardPE']
@ -79,6 +85,7 @@ async def get_data(ticker):
'eps': company_quote['eps'],
'sharesOutstanding': company_quote['sharesOutstanding'],
'forwardPE': forward_pe,
'score': score,
'previousClose': company_quote['price'], #This is true because I update my db before the market opens hence the price will be the previousClose price.
'website': company_profile[0]['website'],
'description': company_profile[0]['description'],

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app/ml_models/__pycache__/score_model.cpython-310.pyc
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6
app/ml_models/score_model.py
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@ -38,11 +38,11 @@ class ScorePredictor:
clear_session()
# Input layer
inputs = Input(shape=(15,))
inputs = Input(shape=(139,))
# First dense layer
x = Dense(64, activation='leaky_relu')(inputs)
x = Dropout(0.3)(x)
x = Dense(128, activation='leaky_relu')(inputs)
x = Dropout(0.5)(x)
x = BatchNormalization()(x)
# Additional dense layers