LSTM.py

@@ -0,0 +1,222 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, LSTM
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+#### Calculate the metrics RMSE and MAPE ####
+def calculate_rmse(y_true, y_pred):
+    """
+    Calculate the Root Mean Squared Error (RMSE)
+    """
+    rmse = np.sqrt(np.mean((y_true - y_pred) ** 2))
+    return rmse
+
+
+def calculate_mape(y_true, y_pred):
+    """
+    Calculate the Mean Absolute Percentage Error (MAPE) %
+    """
+    y_pred, y_true = np.array(y_pred), np.array(y_true)
+    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100
+    return mape
+
+train_quota = 0.8
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+price = stock_data["Close"].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
+
+features = np.vstack((price, volume)).T
+
+# Necessary for MAs
+norm_features = minmax_scaler.fit_transform(price.reshape(-1, 1))
+
+
+# merge data into 2d numpy array
+Y = np.zeros(features.shape[0] - 1)
+
+
+for i in range(Y.size):
+    Y[i] = norm_features[i+1, 0]
+
+time_window = 20
+
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+print(norm_features.shape, Y.shape)
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+def LSTM_model():
+    model = Sequential()
+
+    model.add(LSTM(units = 50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
+    model.add(Dropout(0.2))
+
+    model.add(LSTM(units=50, return_sequences=True))
+    model.add(Dropout(0.2))
+
+    model.add(LSTM(units=50))
+    model.add(Dropout(0.2))
+
+    model.add(Dense(units=1))
+
+    return model
+
+model = LSTM_model()
+model.summary()
+model.compile(
+    optimizer="adam",
+    loss="mean_squared_error"
+)
+
+# Save weights only for best model
+checkpointer = ModelCheckpoint(
+    filepath = 'weights_best.hdf5', 
+    verbose = 2, 
+    save_best_only = True
+)
+
+if os.path.exists("./checkpoints/checkpoint"):
+    model.load_weights("./checkpoints/my_checkpoint")
+else:
+    model.fit(
+        X_train, 
+        Y_train, 
+        epochs=25, 
+        batch_size = 32,
+        callbacks = [checkpointer]
+    )
+    
+    model.save_weights("./checkpoints/my_checkpoint")
+
+prediction = model.predict(X_test)
+predicted_prices = minmax_scaler.inverse_transform(prediction).flatten()
+
+counter = 0
+
+#for i in range(prediction.shape[0]-1):
+#    if (prediction[i+1,] - prediction[i,] > 0 and predicted_prices[i+1,] - predicted_prices[i,] > 0) or (prediction[i+1,] - prediction[i,] < 0 and predicted_prices[i+1,] - predicted_prices[i,] < 0):
+#        counter = counter + 1
+
+#print("acc: ", counter/prediction.shape[0])
+
+
+
+test_prices = price[time_window - 1 + train_size:]
+
+
+pred_ret = []
+actual_ret = []
+for j in range(len(test_prices) - 1):
+    # il predicted price è il prezzo di domani, lo voglio confrontare con il ritorno effettivo di domani
+    pred_ret.append((predicted_prices[j] - test_prices[j])/test_prices[j])
+    actual_ret.append((test_prices[j+1] - test_prices[j])/test_prices[j])
+
+pred_ret_np = np.array(pred_ret)
+actual_ret_np = np.array(actual_ret)
+
+sign_comp = np.sum(np.sign(pred_ret_np) == np.sign(actual_ret_np))/len(pred_ret_np)
+sign_comp_red_nottoomuch = np.sum(np.sign(pred_ret_np[:200]) == np.sign(actual_ret_np[:200]))/len(pred_ret_np[:200])
+sign_comp_red = np.sum(np.sign(pred_ret_np[:100]) == np.sign(actual_ret_np[:100]))/len(pred_ret_np[:100])
+sign_comp_red_alot = np.sum(np.sign(pred_ret_np[:50]) == np.sign(actual_ret_np[:50]))/len(pred_ret_np[:50])
+
+
+print(sign_comp)
+print(sign_comp_red_nottoomuch)
+print(sign_comp_red)
+print(sign_comp_red_alot)
+
+rmse = calculate_rmse(test_prices[1:], predicted_prices)
+mape = calculate_mape(test_prices[1:], predicted_prices)
+
+print("RMSE: ", rmse)
+print("MAPE: ", mape)
+
+rmse = calculate_rmse(test_prices[1:301], predicted_prices[:300])
+mape = calculate_mape(test_prices[1:301], predicted_prices[:300])
+
+print("RMSE su 300 gg: ", rmse)
+print("MAPE su 300 gg: ", mape)
+
+#plt.plot(pred_ret, color=seshadri[0])
+#plt.plot(daily_returns[1:], color=seshadri[1])
+
+fig = plt.figure(1, figsize=(12,10))
+plt.plot(test_prices, color=seshadri[0], label="Registered Closing Price")
+plt.plot(predicted_prices, color=seshadri[1], label="Prediction")
+
+#plot params
+plt.xlim([0,1200])
+plt.ylim([100,400])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Days (from last training)', fontsize=14) 
+plt.ylabel(r'Price (USD)',fontsize=14)  # label the y axis
+
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/First_Attempt_LSTM_2.png", dpi=300)
+
+plt.show()
+#with open("plots/data/MLP_20_10_5_2.csv", "a") as f:
+#    f.write(f"{time_window};{train_score};{score};\n")

LSTM_advanced.py

@@ -0,0 +1,238 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, LSTM, Input
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+np.set_printoptions(threshold=100)
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+#### Calculate the metrics RMSE and MAPE ####
+def calculate_rmse(y_true, y_pred):
+    """
+    Calculate the Root Mean Squared Error (RMSE)
+    """
+    rmse = np.sqrt(np.mean((y_true - y_pred) ** 2))
+    return rmse
+
+
+def calculate_mape(y_true, y_pred):
+    """
+    Calculate the Mean Absolute Percentage Error (MAPE) %
+    """
+    y_pred, y_true = np.array(y_pred), np.array(y_true)
+    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100
+    return mape
+
+train_quota = 0.8
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+price = stock_data["Close"].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
+sec_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
+
+#features = np.vstack((price, volume)).T
+
+# Necessary for MAs
+#norm_features = np.hstack((minmax_scaler.fit_transform(price.reshape(-1, 1)), sec_scaler.fit_transform(volume.reshape(-1, 1))))
+norm_features = minmax_scaler.fit_transform(price.reshape(-1, 1))
+
+rets = np.diff(price)
+bin_rets = np.zeros(len(rets))
+for i, r in enumerate(rets):
+    if r >= 0:
+        bin_rets[i] = 1
+    else:
+        bin_rets[i] = 0
+
+bin_rets_np = np.array(bin_rets)
+
+
+#norm_rets = sec_scaler.fit_transform(rets.reshape(-1, 1))
+
+print("occai")
+
+print(rets)
+print(bin_rets)
+
+print("ocai")
+
+# merge data into 2d numpy array
+#Y = np.zeros(norm_features.shape[0] - 1)
+#for i in range(Y.size):
+#    Y[i] = norm_features[i+1, 0]
+
+Y = bin_rets
+
+time_window = 20
+
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+def LSTM_model():
+    model = Sequential()
+
+    model.add(LSTM(units = 20, return_sequences=True, input_shape=(X_train.shape[1], 1)))
+    model.add(Dropout(0.2))
+
+    #model.add(LSTM(units=50, return_sequences=True))
+    #model.add(Dropout(0.2))
+
+    model.add(LSTM(units=20))
+    model.add(Dropout(0.2))
+
+    model.add(Dense(units=5))
+    model.add(Dropout(0.3))
+
+    model.add(Dense(units=1, activation="sigmoid"))
+
+    return model
+
+model = LSTM_model()
+model.summary()
+model.compile(
+    optimizer="adam",
+    loss="mean_squared_error"
+)
+
+#if os.path.exists("./checkpoints/checkpoint"):
+#    model.load_weights("./checkpoints/my_checkpoint")
+#else:
+model.fit(
+    X_train, 
+    Y_train,
+    shuffle=True,
+    epochs=20,
+    batch_size=20
+)
+    
+    #model.save_weights("./checkpoints/my_checkpoint")
+
+prediction = model.predict(X_test)
+print(prediction)
+print(model.evaluate(X_test, Y_test))
+#predicted_prices = minmax_scaler.inverse_transform(prediction).flatten()
+#predicted_rets = sec_scaler.inverse_transform(prediction).flatten()
+#print(predicted_rets)
+#counter = 0
+#for i in range(prediction.shape[0]-1):
+#    if (prediction[i+1,] - prediction[i,] > 0 and predicted_prices[i+1,] - predicted_prices[i,] > 0) or (prediction[i+1,] - prediction[i,] < 0 and predicted_prices[i+1,] - predicted_prices[i,] < 0):
+#        counter = counter + 1
+
+#print("acc: ", counter/prediction.shape[0])
+
+
+
+#test_prices = price[time_window - 1 + train_size:]
+#pred_ret = []
+#actual_ret = []
+#for j in range(len(test_prices) - 1):
+#    # il predicted price è il prezzo di domani, lo voglio confrontare con il ritorno effettivo di domani
+#    pred_ret.append((predicted_prices[j] - test_prices[j])/test_prices[j])
+#    actual_ret.append((test_prices[j+1] - test_prices[j])/test_prices[j])
+#
+#pred_ret_np = np.array(pred_ret)
+#actual_ret_np = np.array(actual_ret)
+#
+#sign_comp = np.sum(np.sign(pred_ret_np) == np.sign(actual_ret_np))/len(pred_ret_np)
+#sign_comp_red_nottoomuch = np.sum(np.sign(pred_ret_np[:200]) == np.sign(actual_ret_np[:200]))/len(pred_ret_np[:200])
+#sign_comp_red = np.sum(np.sign(pred_ret_np[:100]) == np.sign(actual_ret_np[:100]))/len(pred_ret_np[:100])
+#sign_comp_red_alot = np.sum(np.sign(pred_ret_np[:50]) == np.sign(actual_ret_np[:50]))/len(pred_ret_np[:50])
+#print(sign_comp)
+#print(sign_comp_red_nottoomuch)
+#print(sign_comp_red)
+#print(sign_comp_red_alot)
+
+#rmse = calculate_rmse(test_prices[1:], predicted_prices)
+#mape = calculate_mape(test_prices[1:], predicted_prices)
+#
+#print("RMSE: ", rmse)
+#print("MAPE: ", mape)
+#
+#rmse = calculate_rmse(test_prices[1:301], predicted_prices[:300])
+#mape = calculate_mape(test_prices[1:301], predicted_prices[:300])
+#
+#print("RMSE su 300 gg: ", rmse)
+#print("MAPE su 300 gg: ", mape)
+
+#plt.plot(pred_ret, color=seshadri[0])
+#plt.plot(daily_returns[1:], color=seshadri[1])
+
+fig = plt.figure(1, figsize=(12,10))
+plt.plot(Y_test, color=seshadri[0], label="Registered Closing Price")
+plt.plot(prediction, color=seshadri[1], label="Prediction")
+
+#plot params
+#plt.xlim([0,450])
+#plt.ylim([-0.5,16])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Days (from last training)', fontsize=14) 
+plt.ylabel(r'Price (USD)',fontsize=14)  # label the y axis
+
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/LSTM_advanced_rets_1.png", dpi=300)
+
+plt.show()
+#with open("plots/data/MLP_20_10_5_2.csv", "a") as f:
+#    f.write(f"{time_window};{train_score};{score};\n")

LSTM_advanced_returns.py

@@ -0,0 +1,230 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, LSTM, Input
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+np.set_printoptions(threshold=1000000)
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+
+train_quota = 0.8
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+price = stock_data["Close"].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+
+#minmax_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
+minmax_scaler = preprocessing.StandardScaler()
+sec_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
+
+#EMA_20 = stock_data["Close"].ewm(span=20, adjust=False).mean()
+#EMA_50 = stock_data["Close"].ewm(span=50, adjust=False).mean()
+#EMAs = np.vstack((EMA_20, EMA_50)).T
+#norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 2)
+
+#EMA_200 = stock_data["Close"].ewm(span=200, adjust=False).mean()
+#EMAs = np.vstack((EMA_20, EMA_50, EMA_200)).T
+#norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 3)
+
+# Necessary for MAs
+#norm_features = np.hstack((minmax_scaler.fit_transform(price.reshape(-1, 1)), sec_scaler.fit_transform(volume.reshape(-1, 1))))
+norm_features = minmax_scaler.fit_transform(np.vstack((price, volume)).T)
+#norm_features = np.hstack((norm_features, norm_EMAs))
+
+rets = np.diff(price)
+bin_rets = np.zeros(len(rets))
+for i, r in enumerate(rets):
+    if r >= 0:
+        bin_rets[i] = 1
+    else:
+        bin_rets[i] = 0
+
+bin_rets_np = np.array(bin_rets)
+
+
+#norm_rets = sec_scaler.fit_transform(rets.reshape(-1, 1))
+
+print("occai")
+
+print(rets)
+print(bin_rets)
+
+print("ocai")
+
+# merge data into 2d numpy array
+#Y = np.zeros(norm_features.shape[0] - 1)
+#for i in range(Y.size):
+#    Y[i] = norm_features[i+1, 0]
+
+Y = bin_rets
+
+time_window = 3
+
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size].reshape(-1, 1)
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:].reshape(-1, 1)
+
+def LSTM_model():
+    model = Sequential()
+
+    model.add(LSTM(units = 20, input_shape=(X_train.shape[1], 1)))
+    #model.add(Dense(units = 20, activation="relu", input_shape=(X_train.shape[1],)))
+    #model.add(Dropout(0.3))
+
+    #model.add(LSTM(units=50, return_sequences=True))
+    #model.add(Dropout(0.2))
+
+
+    model.add(Dense(units=10, activation="relu"))
+
+    model.add(Dense(units=5, activation="relu"))
+
+    model.add(Dense(units=1, activation="sigmoid"))
+
+    return model
+
+model = LSTM_model()
+model.summary()
+model.compile(
+    optimizer="adam",
+    loss="binary_crossentropy",
+    metrics=['accuracy']
+)
+
+#if os.path.exists("./checkpoints/checkpoint"):
+#    model.load_weights("./checkpoints/my_checkpoint")
+#else:
+model.fit(
+    X_train, 
+    Y_train,
+    shuffle=True,
+    epochs=50,
+    batch_size=32
+)
+    
+    #model.save_weights("./checkpoints/my_checkpoint")
+
+prediction = model.predict(X_test).flatten()
+print("pred: ", prediction)
+print(model.evaluate(X_test, Y_test))
+#predicted_prices = minmax_scaler.inverse_transform(prediction).flatten()
+#predicted_rets = sec_scaler.inverse_transform(prediction).flatten()
+#print(predicted_rets)
+#counter = 0
+#for i in range(prediction.shape[0]-1):
+#    if (prediction[i+1,] - prediction[i,] > 0 and predicted_prices[i+1,] - predicted_prices[i,] > 0) or (prediction[i+1,] - prediction[i,] < 0 and predicted_prices[i+1,] - predicted_prices[i,] < 0):
+#        counter = counter + 1
+
+#print("acc: ", counter/prediction.shape[0])
+
+
+
+#test_prices = price[time_window - 1 + train_size:]
+#pred_ret = []
+#actual_ret = []
+#for j in range(len(test_prices) - 1):
+#    # il predicted price è il prezzo di domani, lo voglio confrontare con il ritorno effettivo di domani
+#    pred_ret.append((predicted_prices[j] - test_prices[j])/test_prices[j])
+#    actual_ret.append((test_prices[j+1] - test_prices[j])/test_prices[j])
+#
+#pred_ret_np = np.array(pred_ret)
+#actual_ret_np = np.array(actual_ret)
+#
+#sign_comp = np.sum(np.sign(pred_ret_np) == np.sign(actual_ret_np))/len(pred_ret_np)
+#sign_comp_red_nottoomuch = np.sum(np.sign(pred_ret_np[:200]) == np.sign(actual_ret_np[:200]))/len(pred_ret_np[:200])
+#sign_comp_red = np.sum(np.sign(pred_ret_np[:100]) == np.sign(actual_ret_np[:100]))/len(pred_ret_np[:100])
+#sign_comp_red_alot = np.sum(np.sign(pred_ret_np[:50]) == np.sign(actual_ret_np[:50]))/len(pred_ret_np[:50])
+#print(sign_comp)
+#print(sign_comp_red_nottoomuch)
+#print(sign_comp_red)
+#print(sign_comp_red_alot)
+
+#rmse = calculate_rmse(test_prices[1:], predicted_prices)
+#mape = calculate_mape(test_prices[1:], predicted_prices)
+#
+#print("RMSE: ", rmse)
+#print("MAPE: ", mape)
+#
+#rmse = calculate_rmse(test_prices[1:301], predicted_prices[:300])
+#mape = calculate_mape(test_prices[1:301], predicted_prices[:300])
+#
+#print("RMSE su 300 gg: ", rmse)
+#print("MAPE su 300 gg: ", mape)
+
+#plt.plot(pred_ret, color=seshadri[0])
+#plt.plot(daily_returns[1:], color=seshadri[1])
+
+fig = plt.figure(1, figsize=(12,10))
+plt.plot(Y_test, color=seshadri[0], label="Registered Closing Price")
+plt.plot(prediction, color=seshadri[1], label="Prediction")
+
+#plot params
+#plt.xlim([0,450])
+#plt.ylim([-0.5,16])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Days (from last training)', fontsize=14) 
+plt.ylabel(r'Price (USD)',fontsize=14)  # label the y axis
+
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/LSTM_advanced_rets_1.png", dpi=300)
+
+plt.show()
+#with open("plots/data/MLP_20_10_5_2.csv", "a") as f:
+#    f.write(f"{time_window};{train_score};{score};\n")

MultiLayer_Perceptron.py

@@ -0,0 +1,119 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+train_quota = 0.8
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy()
+prices = stock_data[["Open", "High", "Low", "Close"]].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler()
+std_scaler = preprocessing.StandardScaler()
+
+features = np.vstack((daily_returns, volume)).T
+
+# Necessary for MAs
+part_features = std_scaler.fit_transform(features)
+
+# Aggiunta EMA
+EMA_20 = stock_data["Close"].ewm(span=20, adjust=False).mean()
+EMA_50 = stock_data["Close"].ewm(span=50, adjust=False).mean()
+EMAs = np.vstack((EMA_20, EMA_50)).T
+norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 2)
+
+#EMA_200 = stock_data["Close"].ewm(span=200, adjust=False).mean()
+#EMAs = np.vstack((EMA_20, EMA_50, EMA_200)).T
+#norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 3)
+norm_features = np.hstack((part_features, norm_EMAs))
+
+
+# merge data into 2d numpy array
+Y = np.zeros(features.shape[0] - 1)
+
+
+for i in range(Y.size):
+    if daily_returns[i+1] >= 0:
+        Y[i] = 1
+    else:
+        Y[i] = 0
+
+# per quando su usano ma fino a 200
+#Y = Y[49:]
+#Y = Y[199:]
+
+print(norm_features.shape, Y.shape)
+
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+
+
+# Iterations vs Accuracy plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, acc_array)
+#plt.xlabel("Iterations")
+#plt.ylabel("Accuracy")
+#
+## Iterations vs Loss plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, losses)
+#plt.xlabel("Iterations")
+#plt.ylabel("Losses")
+#
+#plt.show()
+
+
+
+#lets try sklearn
+from sklearn.neural_network import MLPClassifier
+#classifier = LogisticRegression(random_state=0, solver="saga").fit(X_train, Y_train)
+clf = MLPClassifier(hidden_layer_sizes=(20,10,5,2), max_iter=30000, verbose=True).fit(X_train, Y_train)
+train_score = clf.score(X_train, Y_train)
+score = clf.score(X_test, Y_test)
+print("sklearn score, all default: ", score, " train ", train_score)
+
+with open("plots/data/MLP_20_10_5_2.csv", "a") as f:
+    f.write(f"{time_window};{train_score};{score};\n")

README.md

@@ -0,0 +1,7 @@
+# Stock Price Prediction
+
+This is a simple project of a stock price prediction tool that tests various configurations.
+
+The final goal is to study performance improvement changing the technology and the methods used.
+
+Final try

appunti.md

@@ -0,0 +1,145 @@
+# Appunti sullo sviluppo del progetto
+
+Il titolo di riferimento per la prima parte di studio è Microsoft. Perché:
+* molto capitalizzato
+* longevo
+* è un titolo tech ma non subisce dinamiche troppo "strane" rispetto al normale andamento di mercato (es. Tesla)
+
+Per prima cosa si testa la performance di un modello non trainato, semplice, che prova a predire il segno del ritorno del giorno successivo sulla base del ritorno del giorno precedente (+ segue + e - segue -).
+
+Si riporta anche un piccolo grafico a barre per avere un'idea della distribuzione dei ritorni.
+
+winrate detected: 0.47638123852445335
+
+Primi test con logistic regression, aggiungendo come features giorni passati: lieve increase, troppi giorni porta a overfitting
+
+provare a effettuare lo stesso test ma aggiungendo qualche metrica (es. moving average) -> C'è un lieve miglioramento
+
+Nell'MLP, nei nomi dei file di dati, i numeri sono la dimensione degli hidden layer, in ordine di profondità
+Primo test semplice semplice, architettura di seguito:
+Model: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ lstm (LSTM)                 (None, 20, 50)            10400     
+                                                                 
+ dropout (Dropout)           (None, 20, 50)            0         
+                                                                 
+ lstm_1 (LSTM)               (None, 20, 50)            20200     
+                                                                 
+ dropout_1 (Dropout)         (None, 20, 50)            0         
+                                                                 
+ lstm_2 (LSTM)               (None, 50)                20200     
+                                                                 
+ dropout_2 (Dropout)         (None, 50)                0         
+                                                                 
+ dense (Dense)               (None, 1)                 51        
+                                                                 
+=================================================================
+Total params: 50,851
+Trainable params: 50,851
+Non-trainable params: 0
+semplici 25 epoche e split 0.8 / 0.2
+il plot che si ottiene è quello
+
+con dati (win rate sui ritorni):
+tutto il testing set (): 0.4991624790619765
+primi 200 giorni: 0.605
+primi 100 giorni: 0.58
+primi 50 giorni: 0.66
+
+su tutto ho 
+RMSE:  76.4 (dollari ?)
+MAPE:  21.8 %
+
+su 300 giorni:
+RMSE su 300 gg:  6.4 $
+MAPE su 300 gg:  2.9 %
+
+In presentazione metto prima grafico con mape e rmse su tutto facendo considerazioni, poi mi allargo con mape e rmse specifiche + winrate su meno giorni
+
+
+nel firts advanced l'architettura è:
+Model: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ lstm (LSTM)                 (None, 10, 10)            480       
+                                                                 
+ dropout (Dropout)           (None, 10, 10)            0         
+                                                                 
+ lstm_1 (LSTM)               (None, 10)                840       
+                                                                 
+ dropout_1 (Dropout)         (None, 10)                0         
+                                                                 
+ dense (Dense)               (None, 5)                 55        
+                                                                 
+ dropout_2 (Dropout)         (None, 5)                 0         
+                                                                 
+ dense_1 (Dense)             (None, 1)                 6         
+                                                                 
+=================================================================
+Total params: 1,381
+Trainable params: 1,381
+Non-trainable params: 0
+_________________________________________________________________
+
+
+LTSM advanced 2: training data ridotta a 2000 giorni, arch:
+Model: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ lstm (LSTM)                 (None, 10, 20)            1760      
+                                                                 
+ dropout (Dropout)           (None, 10, 20)            0         
+                                                                 
+ lstm_1 (LSTM)               (None, 20)                3280      
+                                                                 
+ dropout_1 (Dropout)         (None, 20)                0         
+                                                                 
+ dense (Dense)               (None, 5)                 105       
+                                                                 
+ dropout_2 (Dropout)         (None, 5)                 0         
+                                                                 
+ dense_1 (Dense)             (None, 1)                 6         
+                                                                 
+=================================================================
+Total params: 5,151
+Trainable params: 5,151
+Non-trainable params: 0
+
+risultati
+RMSE:  10.799429328578809
+MAPE:  3.1894335488381116
+RMSE su 300 gg:  11.607057105021592
+MAPE su 300 gg:  3.591834377775106
+
+training di 50 epoche
+ma win rate sul ritorno del giorno dopo sempre ~0.5
+
+
+Il 3 ha un'architettura molto semplificata e tiene una timewindow di soli 5 gg:
+Model: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ lstm (LSTM)                 (None, 10)                480       
+                                                                 
+ dropout (Dropout)           (None, 10)                0         
+                                                                 
+ dense (Dense)               (None, 1)                 11        
+                                                                 
+=================================================================
+Total params: 491
+Trainable params: 491
+Non-trainable params: 0
+_________________________________________________________________
+RMSE:  12.955399161548117
+MAPE:  3.7480157718302904
+RMSE su 300 gg:  11.019121338505466
+MAPE su 300 gg:  3.3382726092879706
+
+non si guadagna molto in winrate
+
+semilog histo

autocorr_plot.py

@@ -0,0 +1,134 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy() * 100
+prices = stock_data[["Open", "High", "Low", "Close"]].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler()
+std_scaler = preprocessing.StandardScaler()
+
+features = np.vstack((daily_returns, volume)).T
+
+# Scale volume data to obtain better results
+#minmax_scaler = preprocessing.MinMaxScaler()
+#norm_ret = std_scaler.fit_transform(daily_returns.reshape(-1,1)).flatten()
+#norm_vol = minmax_scaler.fit_transform(volume.reshape(-1,1)).flatten()
+#norm_features = np.vstack((norm_ret, norm_vol)).T
+
+# Solo volumi e ritorni
+#norm_features = std_scaler.fit_transform(features)
+
+# Aggiunta di prezzi
+#norm_prices = minmax_scaler.fit_transform(prices.reshape(-1, 1)).reshape(-1, 4)
+#norm_ret_and_vol = std_scaler.fit_transform(features)
+#norm_features = np.hstack((norm_ret_and_vol, norm_prices))
+
+# Necessary for MAs
+part_features = std_scaler.fit_transform(features)
+
+# Aggiunta SMA
+#SMA_20 = stock_data["Close"].rolling(20).mean().to_numpy()
+#SMA_50 = stock_data["Close"].rolling(50).mean().to_numpy()
+#SMA_200 = stock_data["Close"].rolling(200).mean().to_numpy()
+#SMAs = np.vstack((SMA_20, SMA_50)).T
+#norm_SMAs = minmax_scaler.fit_transform(SMAs[49:, ].reshape(-1, 1)).reshape(-1, 2)
+#norm_features = np.hstack((part_features[49:, ], norm_SMAs))
+
+#SMAs = np.vstack((SMA_20, SMA_50, SMA_200)).T
+#norm_SMAs = minmax_scaler.fit_transform(SMAs[199:, ].reshape(-1, 1)).reshape(-1, 3)
+#norm_features = np.hstack((part_features[199:, ], norm_SMAs))
+
+# Aggiunta EMA
+EMA_20 = stock_data["Close"].ewm(span=20, adjust=False).mean()
+EMA_50 = stock_data["Close"].ewm(span=50, adjust=False).mean()
+EMAs = np.vstack((EMA_20, EMA_50)).T
+norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 2)
+
+#EMA_200 = stock_data["Close"].ewm(span=200, adjust=False).mean()
+#EMAs = np.vstack((EMA_20, EMA_50, EMA_200)).T
+#norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 3)
+norm_features = np.hstack((part_features, norm_EMAs))
+
+dfeat = {"Daily Returns" : norm_features[:,0],
+"Volume" : norm_features[:,1],
+"EMA20" : norm_features[:,2],
+"EMA50" : norm_features[:,3] 
+}
+
+corr = pd.DataFrame(dfeat).corr()
+fig = plt.figure(1, (11, 10))
+sns.heatmap(corr, vmin=-1, vmax=1, center=0, cmap="mako")
+plt.tick_params(labelsize=14)
+
+plt.savefig("plots/Correlation_EMAs.png", dpi=300)
+
+# merge data into 2d numpy array
+Y = np.zeros(features.shape[0] - 1)
+
+
+for i in range(Y.size):
+    if daily_returns[i+1] >= 0:
+        Y[i] = 1
+    else:
+        Y[i] = 0
+
+# per quando su usano ma fino a 200
+#Y = Y[49:]
+#Y = Y[199:]
+
+print(norm_features.shape, Y.shape)
+
+fig, ax = plt.subplots(figsize=(15,10))
+
+
+#plot params
+#plt.xlim([-12,12])
+#plt.ylim([-0.5,16])
+ax.minorticks_on()
+ax.tick_params(labelsize=14)
+ax.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+ax.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+ax.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+ax.set_xlim([0, 500])
+#ax.set_ylim([-0.5, 0.5])
+
+
+
+
+pd.plotting.autocorrelation_plot(daily_returns, ax=ax, color=seshadri[0], label="Daily Returns")
+pd.plotting.autocorrelation_plot(np.abs(daily_returns), ax=ax, color=seshadri[1], label="Absolute Daily Returns")
+pd.plotting.autocorrelation_plot(volume, ax=ax, color=seshadri[2], label="Volume")
+
+ax.grid(False)
+ax.set_xlabel(r'Lag', fontsize=14) 
+ax.set_ylabel(r'Autocorrelation',fontsize=14)  # label the y axis
+
+
+ax.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+plt.savefig("plots/Autocorrelation_returns_volume_abs.png", dpi=300)

logistic_regression.py

@@ -0,0 +1,228 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+train_quota = 0.8
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+
+def logreg_inference(x, w, b):
+    z = (x @ w) + b
+    p = sigmoid(z)
+    return p
+
+
+def cross_entropy(P, Y):
+    return (-Y * np.log(P) - (1 - Y) * np.log(1 - P)).mean()
+
+
+def logreg_train(X, Y, lambda_, lr = 1e-4, steps=100000):
+    # The training samples are defined as such (each row of X is a sample):
+    # X[0, :] -> Y[0]
+    # X[1, :] -> Y[1]
+
+    m, n = X.shape
+
+    # Initial values for the parameters
+    w = np.zeros(n)
+    b = 0
+
+    # Initial values for the "precedent loss" and "convergence" variables, used to check convergence
+    prec_loss = 0
+    convergence = 0
+
+    for step in range(steps):
+        P = logreg_inference(X, w, b)
+        loss = cross_entropy(P, Y)
+        
+
+        if step % 1000 == 0:
+            print(step, loss)
+
+            # Difference between "precedent loss" and "current loss"
+            diff = np.absolute(prec_loss - loss)
+            prec_loss = loss
+            if diff < 0.00001:
+                # If convergence is reached, the algorithm is stopped
+                convergence = step
+                break
+
+        # Derivative of the loss function with respect to bias
+        grad_b = (P - Y).mean()
+
+        # Gradient of the loss function with respect to weights
+        grad_w = (X.T @ (P - Y)) / m
+
+        w -= lr * grad_w
+        b -= lr * grad_b
+
+        # Every 100 iteration the values of accuracy and loss are saved for plotting
+        if step%100 == 0:
+            Yhat = (P > 0.5)
+            acc_array.append((Y == Yhat).mean() * 100)
+            losses.append(loss)
+
+    # Print the iterations needed for convergence before returning
+    print("Convergence = ", convergence)
+
+    return w, b
+
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy()
+prices = stock_data[["Open", "High", "Low", "Close"]].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler()
+std_scaler = preprocessing.StandardScaler()
+
+features = np.vstack((daily_returns, volume)).T
+
+# Scale volume data to obtain better results
+#minmax_scaler = preprocessing.MinMaxScaler()
+#norm_ret = std_scaler.fit_transform(daily_returns.reshape(-1,1)).flatten()
+#norm_vol = minmax_scaler.fit_transform(volume.reshape(-1,1)).flatten()
+#norm_features = np.vstack((norm_ret, norm_vol)).T
+
+# Solo volumi e ritorni
+#norm_features = std_scaler.fit_transform(features)
+
+# Aggiunta di prezzi
+#norm_prices = minmax_scaler.fit_transform(prices.reshape(-1, 1)).reshape(-1, 4)
+#norm_ret_and_vol = std_scaler.fit_transform(features)
+#norm_features = np.hstack((norm_ret_and_vol, norm_prices))
+
+# Necessary for MAs
+part_features = std_scaler.fit_transform(features)
+
+# Aggiunta SMA
+#SMA_20 = stock_data["Close"].rolling(20).mean().to_numpy()
+#SMA_50 = stock_data["Close"].rolling(50).mean().to_numpy()
+#SMA_200 = stock_data["Close"].rolling(200).mean().to_numpy()
+#SMAs = np.vstack((SMA_20, SMA_50)).T
+#norm_SMAs = minmax_scaler.fit_transform(SMAs[49:, ].reshape(-1, 1)).reshape(-1, 2)
+#norm_features = np.hstack((part_features[49:, ], norm_SMAs))
+
+#SMAs = np.vstack((SMA_20, SMA_50, SMA_200)).T
+#norm_SMAs = minmax_scaler.fit_transform(SMAs[199:, ].reshape(-1, 1)).reshape(-1, 3)
+#norm_features = np.hstack((part_features[199:, ], norm_SMAs))
+
+# Aggiunta EMA
+EMA_20 = stock_data["Close"].ewm(span=20, adjust=False).mean()
+EMA_50 = stock_data["Close"].ewm(span=50, adjust=False).mean()
+EMAs = np.vstack((EMA_20, EMA_50)).T
+norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 2)
+
+#EMA_200 = stock_data["Close"].ewm(span=200, adjust=False).mean()
+#EMAs = np.vstack((EMA_20, EMA_50, EMA_200)).T
+#norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 3)
+norm_features = np.hstack((part_features, norm_EMAs))
+
+
+# merge data into 2d numpy array
+Y = np.zeros(features.shape[0] - 1)
+
+
+for i in range(Y.size):
+    if daily_returns[i+1] >= 0:
+        Y[i] = 1
+    else:
+        Y[i] = 0
+
+# per quando su usano ma fino a 200
+#Y = Y[49:]
+#Y = Y[199:]
+
+print(norm_features.shape, Y.shape)
+
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+#if time_window > 1:
+#    X_train = enlarge_lag(X_train)
+#    Y_train = Y_train[time_window-1:]
+#
+#    X_test = enlarge_lag(X_test)
+#    Y_test = Y_test[time_window-1:]
+
+
+# Lists to save accuracy and loss
+acc_array = []
+losses = []
+
+w, b = logreg_train(X_train, Y_train, 0.0, 1e-3, 1000000)
+print("Weights: ", w)
+print("Bias: ", b)
+
+# Iterations vs Accuracy plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, acc_array)
+#plt.xlabel("Iterations")
+#plt.ylabel("Accuracy")
+#
+## Iterations vs Loss plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, losses)
+#plt.xlabel("Iterations")
+#plt.ylabel("Losses")
+#
+#plt.show()
+# Training accuracy of the model, is the last value recorded in the array
+print("Training Acc: ", acc_array[-1])
+
+P_test = logreg_inference(X_test, w, b)
+Yhat_test = (P_test > 0.5)
+accuracy_test = (Y_test == Yhat_test).mean()
+print("Test accuracy: ", 100*accuracy_test)
+
+
+#lets try sklearn
+#from sklearn.linear_model import LogisticRegression
+#classifier = LogisticRegression(random_state=0, solver="saga").fit(X_train, Y_train)
+#score = classifier.score(X_test, Y_test)
+#print("sklearn score, all default: ", score)
+
+with open("plots/data/logistic_regression_EMA_20_50.csv", "a") as f:
+    f.write(f"{time_window};{acc_array[-1]};{accuracy_test};\n")

logistic_regression_enlarge_only_rets.py

@@ -0,0 +1,203 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+train_quota = 0.8
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+
+def logreg_inference(x, w, b):
+    z = (x @ w) + b
+    p = sigmoid(z)
+    return p
+
+
+def cross_entropy(P, Y):
+    return (-Y * np.log(P) - (1 - Y) * np.log(1 - P)).mean()
+
+
+def logreg_train(X, Y, lambda_, lr = 1e-4, steps=100000):
+    # The training samples are defined as such (each row of X is a sample):
+    # X[0, :] -> Y[0]
+    # X[1, :] -> Y[1]
+
+    m, n = X.shape
+
+    # Initial values for the parameters
+    w = np.zeros(n)
+    b = 0
+
+    # Initial values for the "precedent loss" and "convergence" variables, used to check convergence
+    prec_loss = 0
+    convergence = 0
+
+    for step in range(steps):
+        P = logreg_inference(X, w, b)
+        loss = cross_entropy(P, Y)
+        
+
+        if step % 1000 == 0:
+            print(step, loss)
+
+            # Difference between "precedent loss" and "current loss"
+            diff = np.absolute(prec_loss - loss)
+            prec_loss = loss
+            if diff < 0.00001:
+                # If convergence is reached, the algorithm is stopped
+                convergence = step
+                break
+
+        # Derivative of the loss function with respect to bias
+        grad_b = (P - Y).mean()
+
+        # Gradient of the loss function with respect to weights
+        grad_w = (X.T @ (P - Y)) / m
+
+        w -= lr * grad_w
+        b -= lr * grad_b
+
+        # Every 100 iteration the values of accuracy and loss are saved for plotting
+        if step%100 == 0:
+            Yhat = (P > 0.5)
+            acc_array.append((Y == Yhat).mean() * 100)
+            losses.append(loss)
+
+    # Print the iterations needed for convergence before returning
+    print("Convergence = ", convergence)
+
+    return w, b
+
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy()
+prices = stock_data[["Open", "High", "Low", "Close"]].to_numpy()
+volume = stock_data["Volume"].to_numpy()
+
+minmax_scaler = preprocessing.MinMaxScaler()
+std_scaler = preprocessing.StandardScaler()
+
+features = np.vstack((daily_returns, volume)).T
+
+# Necessary for MAs
+part_features = std_scaler.fit_transform(features)
+
+# merge data into 2d numpy array
+Y = np.zeros(features.shape[0] - 1)
+
+for i in range(Y.size):
+    if daily_returns[i+1] >= 0:
+        Y[i] = 1
+    else:
+        Y[i] = 0
+
+import copy
+
+if time_window > 1:
+    large_rets = enlarge_lag(part_features[:, 0].reshape(-1, 1), time_window)
+    Y = Y[time_window-1:]
+else:
+    large_rets = copy.deepcopy(part_features[:, 0].reshape(-1, 1))
+
+part_features = np.hstack((large_rets, part_features[time_window-1:, 1].reshape(-1, 1)))
+
+
+# Aggiunta EMA
+EMA_20 = stock_data["Close"].ewm(span=20, adjust=False).mean()
+EMA_50 = stock_data["Close"].ewm(span=50, adjust=False).mean()
+EMAs = np.vstack((EMA_20, EMA_50)).T
+norm_EMAs = minmax_scaler.fit_transform(EMAs.reshape(-1, 1)).reshape(-1, 2)
+
+norm_features = np.hstack((part_features, norm_EMAs[time_window-1:,]))
+
+
+print(norm_features.shape, Y.shape)
+
+
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+#if time_window > 1:
+#    X_train = enlarge_lag(X_train)
+#    Y_train = Y_train[time_window-1:]
+#
+#    X_test = enlarge_lag(X_test)
+#    Y_test = Y_test[time_window-1:]
+
+
+# Lists to save accuracy and loss
+acc_array = []
+losses = []
+
+w, b = logreg_train(X_train, Y_train, 0.0, 1e-3, 1000000)
+print("Weights: ", w)
+print("Bias: ", b)
+
+# Iterations vs Accuracy plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, acc_array)
+#plt.xlabel("Iterations")
+#plt.ylabel("Accuracy")
+#
+## Iterations vs Loss plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, losses)
+#plt.xlabel("Iterations")
+#plt.ylabel("Losses")
+#
+#plt.show()
+# Training accuracy of the model, is the last value recorded in the array
+print("Training Acc: ", acc_array[-1])
+
+P_test = logreg_inference(X_test, w, b)
+Yhat_test = (P_test > 0.5)
+accuracy_test = (Y_test == Yhat_test).mean()
+print("Test accuracy: ", 100*accuracy_test)
+
+
+#lets try sklearn
+#from sklearn.linear_model import LogisticRegression
+#classifier = LogisticRegression(random_state=0, solver="saga").fit(X_train, Y_train)
+#score = classifier.score(X_test, Y_test)
+#print("sklearn score, all default: ", score)
+
+with open("plots/data/logistic_regression_EMA_20_50_only_daily_enlarged.csv", "a") as f:
+    f.write(f"{time_window};{acc_array[-1]};{accuracy_test};\n")

logistic_regression_only_returns.py

@@ -0,0 +1,170 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import yfinance as yf
+from datetime import datetime
+import os, sys
+
+from sklearn import preprocessing
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+train_quota = 0.8
+
+def enlarge_lag(to_enlarge, time_window=1):
+    # to_enlarge is the data already present, should be a numpy array
+    enlarged = []
+    for i in range(to_enlarge.shape[0] - time_window + 1):
+        new_element = []
+        for j in range(time_window):
+            new_element.extend(to_enlarge[i + time_window - 1 - j, :])
+        enlarged.append(new_element)
+
+    return np.array(enlarged)
+
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+
+def logreg_inference(x, w, b):
+    z = (x @ w) + b
+    p = sigmoid(z)
+    return p
+
+
+def cross_entropy(P, Y):
+    return (-Y * np.log(P) - (1 - Y) * np.log(1 - P)).mean()
+
+
+def logreg_train(X, Y, lambda_, lr = 1e-4, steps=100000):
+    # The training samples are defined as such (each row of X is a sample):
+    # X[0, :] -> Y[0]
+    # X[1, :] -> Y[1]
+
+    m, n = X.shape
+
+    # Initial values for the parameters
+    w = np.zeros(n)
+    b = 0
+
+    # Initial values for the "precedent loss" and "convergence" variables, used to check convergence
+    prec_loss = 0
+    convergence = 0
+
+    for step in range(steps):
+        P = logreg_inference(X, w, b)
+        loss = cross_entropy(P, Y)
+        
+
+        if step % 1000 == 0:
+            print(step, loss)
+
+            # Difference between "precedent loss" and "current loss"
+            diff = np.absolute(prec_loss - loss)
+            prec_loss = loss
+            if diff < 0.00001:
+                # If convergence is reached, the algorithm is stopped
+                convergence = step
+                break
+
+        # Derivative of the loss function with respect to bias
+        grad_b = (P - Y).mean()
+
+        # Gradient of the loss function with respect to weights
+        grad_w = (X.T @ (P - Y)) / m
+
+        w -= lr * grad_w
+        b -= lr * grad_b
+
+        # Every 100 iteration the values of accuracy and loss are saved for plotting
+        if step%100 == 0:
+            Yhat = (P > 0.5)
+            acc_array.append((Y == Yhat).mean() * 100)
+            losses.append(loss)
+
+    # Print the iterations needed for convergence before returning
+    print("Convergence = ", convergence)
+
+    return w, b
+
+
+if len(sys.argv) > 1:
+    time_window = int(sys.argv[1])
+else:
+    time_window = 1
+
+#time_window = 10
+
+stock_data = pd.read_pickle("data/MSFT_data.pkl")
+
+daily_returns = ((stock_data["Close"] - stock_data["Open"]) / stock_data["Open"]).to_numpy().reshape(-1,1)
+
+
+# merge data into 2d numpy array
+Y = np.zeros(daily_returns.shape[0] - 1)
+
+print(daily_returns.shape, Y.shape)
+
+for i in range(Y.size):
+    if daily_returns[i+1] >= 0:
+        Y[i] = 1
+    else:
+        Y[i] = 0
+import copy
+norm_features = copy.deepcopy(daily_returns)
+if time_window > 1:
+    norm_features = enlarge_lag(norm_features, time_window)
+    Y = Y[time_window-1:]
+
+train_size = int(norm_features.shape[0] * 0.8)
+X_train = norm_features[:train_size, ]
+Y_train = Y[:train_size]
+
+X_test = norm_features[train_size:-1, ]
+Y_test = Y[train_size:]
+
+
+# Lists to save accuracy and loss
+acc_array = []
+losses = []
+
+w, b = logreg_train(X_train, Y_train, 0.0, 1e-3, 1000000)
+print("Weights: ", w)
+print("Bias: ", b)
+
+# Iterations vs Accuracy plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, acc_array)
+#plt.xlabel("Iterations")
+#plt.ylabel("Accuracy")
+#
+## Iterations vs Loss plot
+#plt.figure()
+#plt.plot(np.arange(0, len(acc_array)) * 100, losses)
+#plt.xlabel("Iterations")
+#plt.ylabel("Losses")
+#
+#plt.show()
+# Training accuracy of the model, is the last value recorded in the array
+print("Training Acc: ", acc_array[-1])
+
+P_test = logreg_inference(X_test, w, b)
+Yhat_test = (P_test > 0.5)
+accuracy_test = (Y_test == Yhat_test).mean()
+print("Test accuracy: ", 100*accuracy_test)
+
+
+#lets try sklearn
+#from sklearn.linear_model import LogisticRegression
+#classifier = LogisticRegression(random_state=0, solver="saga").fit(X_train, Y_train)
+#score = classifier.score(X_test, Y_test)
+#print("sklearn score, all default: ", score)
+
+with open("plots/data/logistic_regression_only_rets.csv", "a") as f:
+    f.write(f"{time_window};{acc_array[-1]};{accuracy_test};\n")

logistic_regression_run_script.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+for i in $(seq 1 50);
+do
+    echo "Running with time window $i"
+    python3 logistic_regression_enlarge_only_rets.py $i
+done
+

mlp_run_script.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+for i in $(seq 1 50);
+do
+    echo "Running with time window $i"
+    python3 MultiLayer_Perceptron.py $i
+done
+

plots/data/MLP_20_10_5_2.csv

@@ -0,0 +1,51 @@
+time_window;training_accuracy;testing_accuracy;
+1;0.5157521385353641;0.5325542570951586;
+2;0.5070951585976627;0.5317195325542571;
+3;0.6231218697829716;0.48955722639933164;
+4;0.6103109997912753;0.4653299916457811;
+5;0.6471816283924844;0.4903926482873851;
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+7;0.6641604010025063;0.5137844611528822;
+8;0.7013366750208856;0.520066889632107;
+9;0.6897848339252142;0.48327759197324416;
+10;0.6136648558295027;0.46321070234113715;
+11;0.6587251828631139;0.4866220735785953;
+12;0.7215719063545151;0.5259197324414716;
+13;0.6684782608695652;0.4811715481171548;
+14;0.748693288730922;0.5188284518828452;
+15;0.740694270179841;0.5154811715481171;
+16;0.6801924283622673;0.47447698744769873;
+17;0.7684100418410041;0.5238493723849372;
+18;0.7566945606694561;0.5008375209380235;
+19;0.7928436911487758;0.5309882747068677;
+20;0.8139388865634156;0.49413735343383586;
+21;0.6206824366757379;0.5301507537688442;
+22;0.7889447236180904;0.507537688442211;
+23;0.6379815745393634;0.4660519698239732;
+24;0.6751832460732984;0.5071248952221291;
+25;0.6258902387934646;0.46437552388935455;
+26;0.7301487534045673;0.49706621961441744;
+27;0.6787510477787091;0.46689019279128247;
+28;0.8658843252305113;0.47651006711409394;
+29;0.7048836721861245;0.535234899328859;
+30;0.8633123689727463;0.5075503355704698;
+31;0.8228140071293772;0.5067114093959731;
+32;0.8181627516778524;0.5058724832214765;
+33;0.6447147651006712;0.5104953820319059;
+34;0.8833647996643591;0.4802686817800168;
+35;0.8063365505665128;0.5071368597816961;
+36;0.8818467995802728;0.5146935348446684;
+37;0.6358102434928632;0.5331654072208228;
+38;0.8717464315701091;0.5117647058823529;
+39;0.9048918748687802;0.47394957983193275;
+40;0.6698866022679546;0.5319327731092437;
+41;0.6784289014912833;0.4689075630252101;
+42;0.7008403361344537;0.5210084033613446;
+43;0.8613445378151261;0.49705634987384356;
+44;0.7066610632485817;0.5021026072329688;
+45;0.6353509878100042;0.5365853658536586;
+46;0.6771074206432626;0.5256518082422204;
+47;0.5971404541631623;0.47434819175777965;
+48;0.8092935239697224;0.4983164983164983;
+49;0.9335436382754995;0.4983164983164983;
+50;0.9091291543962978;0.5244107744107744;

plots/data/MLP_30_20_20_10.csv

@@ -0,0 +1,51 @@
+time_window;training_accuracy;testing_accuracy;
+1;0.547673690799082;0.5217028380634391;
+2;0.6400250417362271;0.508347245409015;
+3;0.715567612687813;0.5472013366750209;
+4;0.7424337299102484;0.4803675856307435;
+5;0.7749478079331942;0.48370927318295737;
+6;0.8275214032157027;0.4954051796157059;
+7;0.8105680868838764;0.4928989139515455;
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plots/data/logistic_regression_SMA_20_50.csv

@@ -0,0 +1,51 @@
+time_window;training_accuracy;testing_accuracy;
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plots/data/logistic_regression_only_rets.csv

@@ -0,0 +1,51 @@
+time_window;training_accuracy;testing_accuracy;
+1;50.69893594825787;0.5317195325542571;
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+49;50.683491062039955;0.5328282828282829;
+50;50.69415229280606;0.5328282828282829;

plots/data/logistic_regression_prices.csv

@@ -0,0 +1,51 @@
+time_window;training_accuracy;testing_accuracy;
+1;51.99248904652618;0.5375626043405676;
+2;51.85726210350584;0.5375626043405676;
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plotter.py

@@ -0,0 +1,61 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+import os
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+data = pd.read_csv("plots/data/MLP_20_10_5_2.csv", sep=";")
+#data = pd.read_csv("plots/data/logistic_regression.csv", sep=";")
+#data_SMA = pd.read_csv("plots/data/logistic_regression_SMA.csv", sep=";")
+#data_SMA_20_50 = pd.read_csv("plots/data/logistic_regression_SMA_20_50.csv", sep=";")
+#data_EMA = pd.read_csv("plots/data/logistic_regression_EMA.csv", sep=";")
+#data_EMA_20_50 = pd.read_csv("plots/data/logistic_regression_EMA_20_50.csv", sep=";")
+
+print(data)
+
+fig = plt.figure(1, figsize=(15,10))
+plt.plot(data["time_window"], data["training_accuracy"]*100, color=seshadri[0], label="Training Accuracy", linewidth=2)
+plt.plot(data["time_window"], data["testing_accuracy"]*100, color=seshadri[1], label="Testing Accuracy", linewidth=2)
+
+
+
+#plt.plot(data["time_window"], data["testing_accuracy"]*100, color=seshadri[0], label="Returns and Volume", linewidth=2)
+#plt.plot(data_SMA_20_50["time_window"], data_SMA_20_50["testing_accuracy"]*100, color=seshadri[1], label="With SMA 20 and 50 candles", linewidth=2)
+#plt.plot(data_SMA["time_window"], data_SMA["testing_accuracy"]*100, color=seshadri[2], label="With SMA 20, 50 and 200 candles", linewidth=2)
+#plt.plot(data_EMA_20_50["time_window"], data_EMA_20_50["testing_accuracy"]*100, color=seshadri[3], label="With EMA 20 and 50 candles", linewidth=2)
+#plt.plot(data_EMA["time_window"], data_EMA["testing_accuracy"]*100, color=seshadri[4], label="With EMA 20, 50 and 200 candles", linewidth=2)
+
+
+#plot params
+plt.xlim([0, 50])
+#plt.ylim([50, 60])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+#plt.grid(True)
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Lag (Days)', fontsize=14) 
+plt.ylabel(r'Accuracy (%)',fontsize=14)  # label the y axis
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/MLP_20_10_5_2.png", dpi=300)
+
+plt.show()

simple_sign_prediction.py

@@ -0,0 +1,91 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+import yfinance as yf
+from datetime import datetime
+
+import os
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+#stock_data = pd.read_csv("data/daily_MSFT.csv").iloc[::-1].reset_index(drop=True)
+
+if os.path.isfile("data/MSFT_data.pkl"):
+    stock_data = pd.read_pickle("data/MSFT_data.pkl")
+elif os.path.isfile("data/MSFT_data.csv"):
+    stock_data = pd.read_csv("data/MSFT_data.csv")
+else:
+    start_date = datetime(2000, 1, 1)
+    end_date = datetime(2023, 10, 26)
+    stock_data = yf.download('MSFT', start=start_date, end=end_date)
+    stock_data.to_pickle("data/MSFT_data.pkl")
+    stock_data.to_csv("data/MSFT_data.csv")
+
+
+
+
+daily_returns = stock_data["Close"] - stock_data["Open"]
+win_lose = np.zeros(daily_returns.size - 1)
+
+for index, return_ in enumerate(daily_returns[:-1]):
+    if (return_ > 0 and daily_returns[index + 1] > 0) or (return_ < 0 and daily_returns[index + 1] < 0):
+        win_lose[index] = 1
+    else:
+        win_lose[index] = 0
+
+
+win_rate = np.count_nonzero(win_lose == 1) / win_lose.size
+
+print(win_rate)
+
+percent_returns = daily_returns / stock_data["Open"] * 100
+
+fig = plt.figure(1, figsize=(15,10))
+plt.hist(percent_returns, bins = 120, range=(-12,12), facecolor=seshadri[0], alpha=0.8, edgecolor="white", label="Percentage daily returns occurrences")
+
+#plt.plot(stock_data.index, stock_data["Close"], linestyle="-", color=seshadri[0])
+#plt.plot(stock_data.index, stock_data["Adj Close"], linestyle="-", color=seshadri[1])
+#plt.plot(stock_data.index, stock_data["close"] - 20, linestyle="-", color=seshadri[2])
+#plt.plot(stock_data.index, stock_data["close"] - 30, linestyle="-", color=seshadri[3])
+#plt.plot(stock_data.index, stock_data["close"] - 40, linestyle="-", color=seshadri[4])
+#plt.plot(stock_data.index, stock_data["close"] - 50, linestyle="-", color=seshadri[5])
+#plt.plot(stock_data.index, stock_data["close"] - 60, linestyle="-", color=seshadri[6])
+#plt.plot(stock_data.index, stock_data["close"] - 70, linestyle="-", color=seshadri[7])
+#plt.show()
+
+
+#plot params
+plt.xlim([-12,12])
+#plt.ylim([-0.5,16])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Percentage daily return', fontsize=14) 
+plt.ylabel(r'Occurrences',fontsize=14)  # label the y axis
+
+plt.yscale('log')
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/MSFT_daily_occs_semilogx.png", dpi=300)
+
+plt.show()
+