import matplotlib.pyplot as plt import streamlit as st import pandas as pd import numpy as np import yfinance as yf import plotly.express as px import plotly.graph_objects as go from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Activation, Dense, Dropout, LSTM from datetime import date, datetime, timedelta from stocknews import StockNews # --- SIDEBAR CODE ticker = st.sidebar.selectbox('Select your Crypto', ["BTC-USD", "ETH-USD"]) start_date = st.sidebar.date_input('Start Date', date.today() - timedelta(days=365)) end_date = st.sidebar.date_input('End Date') # --- MAIN PAGE st.header('Cryptocurrency Prediction') col1, col2, = st.columns([1,9]) with col1: st.image('icons/'+ ticker +'.png', width=75) with col2: st.write(f" ## { ticker}") ticker_obj = yf.Ticker(ticker) # --- CODE model_data = ticker_obj.history(interval='1h', start=start_date, end=end_date) # Extract the 'close' column for prediction target_data = model_data["Close"].values.reshape(-1, 1) # Normalize the target data scaler = MinMaxScaler() target_data_normalized = scaler.fit_transform(target_data) # Normalize the input features input_features = ['Open', 'High', 'Low', 'Volume'] input_data = model_data[input_features].values input_data_normalized = scaler.fit_transform(input_data) def build_lstm_model(input_data, output_size, neurons, activ_func='linear', dropout=0.2, loss='mse', optimizer='adam'): model = Sequential() model.add(LSTM(neurons, input_shape=(input_data.shape[1], input_data.shape[2]))) model.add(Dropout(dropout)) model.add(Dense(units=output_size)) model.add(Activation(activ_func)) model.compile(loss=loss, optimizer=optimizer) return model # Hyperparameters np.random.seed(245) window_len = 10 split_ratio = 0.8 # Ratio of training set to total data zero_base = True lstm_neurons = 50 epochs = 100 batch_size = 128 #32 loss = 'mean_squared_error' dropout = 0.24 optimizer = 'adam' def extract_window_data(input_data, target_data, window_len): X = [] y = [] for i in range(len(input_data) - window_len): X.append(input_data[i : i + window_len]) y.append(target_data[i + window_len]) return np.array(X), np.array(y) X, y = extract_window_data(input_data_normalized, target_data_normalized, window_len) # Split the data into training and testing sets split_ratio = 0.8 # Ratio of training set to total data split_index = int(split_ratio * len(X)) X_train, X_test = X[:split_index], X[split_index:] y_train, y_test = y[:split_index], y[split_index:] # Creating model model = build_lstm_model(X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss, optimizer=optimizer) # Saved Weights file_path = "LSTM_" + ticker + "_weights.h5" # Loads the weights model.load_weights(file_path) # Step 4: Make predictions preds = model.predict(X_test) y_test = y[split_index:] # Normalize the target data scaler = MinMaxScaler() target_data_normalized = scaler.fit_transform(target_data) # Inverse normalize the predictions preds = preds.reshape(-1, 1) y_test = y_test.reshape(-1, 1) preds = scaler.inverse_transform(preds) y_test = scaler.inverse_transform(y_test) fig = px.line(x=model_data.index[-len(y_test):], y=[y_test.flatten(), preds.flatten()]) newnames = {'wide_variable_0':'Real Values', 'wide_variable_1': 'Predictions'} fig.for_each_trace(lambda t: t.update(name = newnames[t.name], legendgroup = newnames[t.name], hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name]))) fig.update_layout( xaxis_title="Date", yaxis_title=ticker+" Price", legend_title=" ") st.write(fig) # --- INFO BUBBLE about_data, news = st.tabs(["About", "News"]) with about_data: # Candlestick raw_data = ticker_obj.history(start=start_date, end=end_date) fig = go.Figure(data=[go.Candlestick(x=raw_data.index, open=raw_data['Open'], high=raw_data['High'], low=raw_data['Low'], close=raw_data['Close'])]) fig.update_layout( title=ticker + " candlestick : Open, High, Low and Close", yaxis_title=ticker + ' Price') st.plotly_chart(fig) # Table history_data = raw_data.copy() # Formating index Date history_data.index = pd.to_datetime(history_data.index, format='%Y-%m-%d %H:%M:%S').date history_data.index.name = "Date" history_data.sort_values(by='Date', ascending=False, inplace=True) st.write(history_data) with news: sNews = StockNews(ticker, save_news=False) sNews_df = sNews.read_rss() # Showing most recent news for i in range(10): st.subheader(f"{i+1} - {sNews_df['title'][i]}") st.write(sNews_df['summary'][i]) date_object = datetime.strptime(sNews_df['published'][i], '%a, %d %b %Y %H:%M:%S %z') st.write(f"_{date_object.strftime('%A')}, {date_object.date()}_")