def get_raw_features(year, quarter, ticker,
path): #we want to predict stock improvement
risk_factors = get_risk_factors(path)
qtr_stock_price = get_avg_qtr_stock_quote(ticker, year, quarter)
improvement = get_next_qtr_stock_quote(ticker, year,
quarter) - qtr_stock_price
features = {'risk_factors': risk_factors, 'stock_improvement': improvement}
return features
def get_predict_features(year, quarter, ticker, path):
risk_factors = get_risk_factors(path)
emotions = emotion_analysis(risk_factors)
sentiment = sentiment_analysis(risk_factors)
qtr_stock_price = get_avg_qtr_stock_quote(ticker,year,quarter)
features = {'anger': emotions['anger'],
'disgust': emotions['disgust'],
'fear': emotions['fear'],
'joy': emotions['joy'],
'sadness': emotions['sadness'],
'sentiment': sentiment,
'sentiment_type': (lambda x : -1 if x < -0.25 else 1 if x > 0.25 else 0)(sentiment)} #(lambda x : 'negatve' if x < -0.25 else 'positive' if x > 0.25 else 'neutral')
return features
def get_training_features(year, quarter, ticker, path): #we want to predict stock improvement
risk_factors = get_risk_factors(path)
emotions = emotion_analysis(risk_factors)
sentiment = sentiment_analysis(risk_factors)
qtr_stock_price = get_avg_qtr_stock_quote(ticker,year,quarter)
improvement = get_next_qtr_stock_quote(ticker,year,quarter) - qtr_stock_price
features = {'anger': emotions['anger'],
'disgust': emotions['disgust'],
'fear': emotions['fear'],
'joy': emotions['joy'],
'sadness': emotions['sadness'],
'sentiment': sentiment,
'sentiment_type': (lambda x : -1 if x < -0.25 else 1 if x > 0.25 else 0)(sentiment), #(lambda x : 'negatve' if x < -0.25 else 'positive' if x > 0.25 else 'neutral')
'stock_improvement': improvement}
return features
def get_last_qtr_stock_quote(ticker, year, qtr):
last_qtr = ((qtr - 2) % 4) + 1
if last_qtr == 4:
year -= 1
return get_avg_qtr_stock_quote(ticker, year, last_qtr)
def get_next_qtr_stock_quote(ticker, year, qtr):
next_qtr = (qtr % 4) + 1
if next_qtr == 1:
year += 1
return get_avg_qtr_stock_quote(ticker, year, next_qtr)