Initialization

app.py

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+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('Omdena Bahrain - 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 = "models/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()}_")

requirements.txt

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+datetime==4.0.1
+numpy==1.22.4
+pandas==1.5.3
+plotly==5.13.1
+sklearn-pandas==2.2.0
+stocknews==0.9.11
+streamlit==1.24.1
+tensorflow==2.12.0
+yfinance==0.2.24