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improvement of ai model

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This commit is contained in:
MuslemRahimi 2024-09-30 21:02:49 +02:00
parent cc60f570bd
commit 85f5dbe075
3 changed files with 15 additions and 15 deletions
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6
app/cron_ai_score.py
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@ -317,7 +317,7 @@ async def process_symbol(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 = ['freeCashFlowYield', 'cci', 'daily_return', 'cashAndCashEquivalents_to_cashAndShortTermInvestments', 'longTermDebt_to_totalLiabilitiesAndStockholdersEquity', 'longTermDebt_to_totalAssets', 'totalStockholdersEquity_to_totalLiabilitiesAndStockholdersEquity', 'totalStockholdersEquity_to_totalAssets']
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']
print(f"For the Ticker: {ticker}")
data = predictor.evaluate_model(test_data[best_features], test_data['Target'])
@ -391,7 +391,7 @@ 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=8)
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'])
@ -406,7 +406,7 @@ async def run():
if train_mode:
#Train first model
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 50E9 AND symbol NOT LIKE '%.%'")
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E9 AND symbol NOT LIKE '%.%'")
stock_symbols = [row[0] for row in cursor.fetchall()]
print('Number of Stocks')
print(len(stock_symbols))

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app/ml_models/__pycache__/score_model.cpython-310.pyc
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24
app/ml_models/score_model.py
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@ -38,24 +38,24 @@ class ScorePredictor:
clear_session()
# Input layer
inputs = Input(shape=(8,))
inputs = Input(shape=(15,))
# First dense layer
x = Dense(512, activation='relu')(inputs)
x = Dropout(0.5)(x)
x = Dense(64, activation='leaky_relu')(inputs)
x = Dropout(0.3)(x)
x = BatchNormalization()(x)
# Additional dense layers
for units in [256,128]:
x = Dense(units, activation='relu')(x)
x = Dropout(0.5)(x)
for units in [64,32]:
x = Dense(units, activation='leaky_relu')(x)
x = Dropout(0.3)(x)
x = BatchNormalization()(x)
# Reshape for attention mechanism
x = Reshape((128, 1))(x)
x = Reshape((32, 1))(x)
# Attention mechanism
attention = Dense(128, activation='relu')(x)
attention = Dense(32, activation='leaky_relu')(x)
attention = Dense(1, activation='softmax')(attention)
# Apply attention
@ -71,7 +71,7 @@ class ScorePredictor:
model = Model(inputs=inputs, outputs=outputs)
# Optimizer with a lower learning rate
optimizer = Adam(learning_rate=0.001, clipnorm=1.0)
optimizer = Adam(learning_rate=0.01, clipnorm=1.0)
# Compile the model
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
@ -95,8 +95,8 @@ class ScorePredictor:
checkpoint = ModelCheckpoint('ml_models/weights/ai-score/weights.keras',
save_best_only=True, save_freq = 1,
monitor='val_loss', mode='min')
early_stopping = EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, min_lr=0.001)
early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=0.001)
self.model.fit(X_train, y_train, epochs=100_000, batch_size=32,
validation_split=0.1, callbacks=[checkpoint, early_stopping, reduce_lr])
@ -112,7 +112,7 @@ class ScorePredictor:
# Get the model's predictions
test_predictions = self.model.predict(X_test)
#print(test_predictions)
print(test_predictions)
# Extract the probabilities for class 1 (index 1 in the softmax output)
class_1_probabilities = test_predictions[:, 1]