ML regression

Machine Learning Scripts /regression.py

@@ -0,0 +1,61 @@
+import quandl, math
+import numpy as np
+import pandas as pd
+from sklearn import preprocessing, svm
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+from matplotlib import style
+from datetime import datetime, timedelta
+import pickle
+import yfinance as yf
+
+style.use('ggplot')
+
+stock = yf.Ticker('FB')
+df = stock.history(period="max")
+df = df[['Open',  'High',  'Low',  'Close', 'Volume']]
+df['HL_PCT'] = (df['High'] - df['Low']) / df['Close'] * 100.0
+df['PCT_change'] = (df['Close'] - df['Open']) / df['Open'] * 100.0
+arr = df['Close']
+df = df[['Close', 'HL_PCT', 'PCT_change', 'Volume']]
+forecast_col = 'Close'
+df.fillna(value=-99999, inplace=True)
+forecast_out = int(math.ceil(0.1 * len(df)))
+df['label'] = df[forecast_col].shift(-forecast_out)
+
+
+X = np.array(df.drop(['label','Close'], 1))
+X = preprocessing.scale(X)
+X_lately = X[-forecast_out:]
+X = X[:-forecast_out]
+
+df.dropna(inplace=True)
+y = np.array(df['label'])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+clf = LinearRegression(n_jobs=-1)
+clf.fit(X_train, y_train)
+
+confidence = clf.score(X_test, y_test)
+print(confidence)
+forecast_set = clf.predict(X_lately)
+df['Forecast'] = np.nan
+
+last_date = df.iloc[-1].name
+last_unix = last_date
+next_unix = last_unix + timedelta(days=1)
+
+for i in forecast_set:
+    next_date = next_unix
+    next_unix += timedelta(days=1)
+    j= [np.nan for _ in range(len(df.columns)-1)]+[i]
+    df.loc[next_date]=j
+#print(df.head())
+arr.plot()
+df['Forecast'].plot()
+plt.legend(loc=4)
+plt.xlabel('Date')
+plt.ylabel('Price')
+plt.show()

Portfolio Optimizing Scripts/sharpe_portfolio_optimizer.py

@@ -48,8 +48,11 @@ def basicStats(df, weights):
 	print('Annual volatility/standard deviation/risk : ',percent_vols)
 	print('Annual variance : ',percent_var)
 	print("\n")
-# def omptimizePortCLA():
-def getDiscrete(df, weights):
+
+def omptimizePortCLA():
+
+
+def getDiscreteAllocations(df, weights):
 	latest_prices = get_latest_prices(df)
 	#plotting.plot_weights(weights)
 	total_portfolio_value  = 15000
@@ -86,15 +89,15 @@ def optimizePortEfficient(port, weights, start, plot = False, short = False, pri
 		print("Weights of an optimal portfolio maximised on Sharpe Ratio:")
 		print(cleaned_weights)
 		ef.portfolio_performance(verbose = True)
-		getDiscrete(df, weights)
+		getDiscreteAllocations(df, weights)
 	if how == "Vol":
 		# Minimized on Volatility
 		efi = EfficientFrontier(mu, S, weight_bounds=(-1,1))
 		w = dict(efi.min_volatility())
 		print("\nWeights of an optimal portfolio minimized on Volatilty (Risk):")
 		print(w)
 		efi.portfolio_performance(verbose = True)
-		getDiscrete(df, w)
+		getDiscreteAllocations(df, w)
 
 	#Current best allocations
 
@@ -103,5 +106,5 @@ def optimizePortEfficient(port, weights, start, plot = False, short = False, pri
 portfolio = ['FB', "AAPL", "AMZN", 'NFLX', 'GOOG']
 weights = np.array([0.2,0.2,0.2,0.2,0.2])
 start = '2013-01-01'
-optimizePortEfficient(portfolio, weights, start)
+optimizePortEfficient(portfolio, weights, start, how = "Vol")