BlazeQuant Challenge Submission AshutoshRajput

dec_2023/AshutoshRajput_/Predictor.py

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+#numpy module is imported for creating arrays with the data
+import numpy as np
+#matplotlib module is imported to graph the prediciton
+import matplotlib.pyplot as plt
+#pandas module is imported to edit the dataset
+import pandas as pd
+#pandas-datareader module is imported to get the prices of the crypto currencies using yahoo finance 
+import pandas_datareader as web
+#datetime module is imported for the calculation of days
+import datetime as dt
+
+#MinMaxScaler is imported from sklearn to scale the data between 0 and 1 for nerual network calculation
+from sklearn.preprocessing import MinMaxScaler
+#tensorflow modules are imported for the neural network creation
+from tensorflow.keras.layers import Dense, Dropout, LSTM
+from tensorflow.keras.models import Sequential
+from tensorflow.python.keras.backend import shape
+
+#The cryptocurrency to be predicted and base currency is defined here
+crypto_currency = "BTC"
+against_currency = "USD"
+
+#Start and end date for test data
+start = dt.datetime(2016, 1, 1)
+end = dt.datetime.now()
+
+#uses DataReader from pandas module to get the historical price data from yahoo finance
+data = web.DataReader(f"{crypto_currency}-{against_currency}", "yahoo", start, end)
+
+#Prepare data
+scaler = MinMaxScaler(feature_range=(0,1))
+scaled_data = scaler.fit_transform(data["Close"].values.reshape(-1,1))
+
+#sets the number of days to be predicted and test data
+#looks at past [prediction_days] to predict
+prediction_days = 60
+future_day = 30
+
+#creates x_train and y_train
+x_train, y_train = [], []
+
+
+for x in range(prediction_days, len(scaled_data)-future_day):
+    #past [prediction_days] and appends the real data to x_train
+    x_train.append(scaled_data[x-prediction_days:x, 0])
+    #the [future_day] is appended as the trained predected data to y_train
+    y_train.append(scaled_data[x+future_day, 0])
+
+#turns x and y train into numpy arrays and then reshapes them
+x_train, y_train = np.array(x_train), np.array(y_train)
+x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
+
+#Create neural network
+
+model = Sequential()
+
+#LSTM layers for feeding data to neural network. Dropout layers to prevent overfitting
+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))
+
+#compiling and fitting the model 
+model.compile(optimizer="adam", loss="mean_squared_error")
+model.fit(x_train, y_train, epochs=25, batch_size=32)
+
+
+#Testing the model
+
+test_start = dt.datetime(2020, 1, 1)
+test_end = dt.datetime.now()
+
+test_data = web.DataReader(f"{crypto_currency}-{against_currency}", "yahoo", test_start, test_end)
+actual_prices = test_data["Close"].values
+
+#combine the test data and actual data
+total_dataset = pd.concat((data["Close"], test_data["Close"]), axis=0)
+
+#places the actual data - test and prediction data into the model input 
+model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values
+model_inputs = model_inputs.reshape(-1, 1)
+model_inputs = scaler.fit_transform(model_inputs)
+
+x_test = []
+
+for x in range(prediction_days, len(model_inputs)):
+    x_test.append(model_inputs[x-prediction_days:x, 0])
+
+x_test = np.array(x_test)
+x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+#Predicts the prices using the model
+prediction_prices = model.predict(x_test)
+prediction_prices = scaler.inverse_transform(prediction_prices)
+
+#Plots them on a graph
+plt.plot(actual_prices, color="black", label="Actual Prices")
+plt.plot(prediction_prices, color="green", label="Predicited Prices")
+plt.title(f"{crypto_currency} Price Prediction")
+plt.xlabel("Time")
+plt.ylabel("Price")
+plt.show()
+
+#Predict next day
+
+real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs) + 1, 0]]
+read_data = np.array(real_data)
+real_data = np.reshape(read_data, (real_data.shape[0], real_data.shape[1], 1))
+
+prediction = model.predict(real_data)
+prediction = scaler.inverse_transform(prediction)
+print()

dec_2023/AshutoshRajput_/README.md

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+# CryptoPricePredictor
+This Program predicts the future price of any cryptocurrency. The green line, which is the prediction, has a 30 offset, meaning you have to move the green line  by 30 days for the prediction.

dec_2023/AshutoshRajput_/requirments.txt

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+numpy
+matplotlib
+pandas
+pandas_datareader
+datetime
+sklearn
+tensorflow