Create app.py

app.py

@@ -0,0 +1,78 @@
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import plotly.graph_objects as go
+import numpy as np
+
+st.sidebar.title('Bali Scalping Strategy Analyzer')
+ticker = st.sidebar.text_input('Enter ticker symbol', 'AAPL')
+start_date = st.sidebar.date_input('Start date', pd.to_datetime('2020-01-01'))
+end_date = st.sidebar.date_input('End date', pd.to_datetime('2020-12-31'))
+time_frame = st.sidebar.selectbox('Select time frame', ['60m', '1d'])
+analyze_button = st.sidebar.button('Analyze')
+
+st.title('Bali Scalping Strategy Visualization')
+st.markdown("""
+This app visualizes the 'Bali' scalping strategy with buy and sell signals.
+Enter the stock ticker, select the date range and time frame, and click 'Analyze'.
+""")
+
+def fetch_data(ticker, start_date, end_date, interval):
+    data = yf.download(ticker, start=start_date, end=end_date, interval=interval)
+    return data
+
+def calculate_lwma(data, period=48):
+    weights = np.arange(1, period + 1)
+    return data.rolling(window=period).apply(lambda prices: np.dot(prices, weights) / weights.sum(), raw=True)
+
+def calculate_trend_envelopes(data, period=2):
+    ma = data.rolling(window=period).mean()
+    deviation = data.rolling(window=period).std()
+    upper_band = ma + (0.02 * deviation)
+    lower_band = ma - (0.02 * deviation)
+    return upper_band, lower_band
+
+def calculate_dss(data, period=10):
+    stoch = ((data - data.rolling(window=period).min()) / 
+             (data.rolling(window=period).max() - data.rolling(window=period).min())) * 100
+    dss = stoch.rolling(window=period).mean()
+    return dss
+
+if analyze_button:
+    data = fetch_data(ticker, start_date, end_date, time_frame)
+    data['LWMA'] = calculate_lwma(data['Close'])
+    upper_band, lower_band = calculate_trend_envelopes(data['Close'])
+    data['DSS'] = calculate_dss(data['Close'])
+
+    data['Buy'] = ((data['Close'] > data['LWMA']) & (data['Close'] > upper_band.shift()) & (data['DSS'] > 80))
+    data['Sell'] = ((data['Close'] < data['LWMA']) & (data['Close'] < lower_band.shift()) & (data['DSS'] < 20))
+    
+    fig = go.Figure()
+    fig.add_trace(go.Candlestick(x=data.index,
+                                 open=data['Open'],
+                                 high=data['High'],
+                                 low=data['Low'],
+                                 close=data['Close'],
+                                 name="Candlestick"))
+    fig.add_trace(go.Scatter(x=data.index, y=data['LWMA'], 
+                             line=dict(color='red', width=1.5), name='LWMA'))
+    fig.add_trace(go.Scatter(x=data.index, y=upper_band, 
+                             line=dict(color='blue', width=0.7), name='Upper Band'))
+    fig.add_trace(go.Scatter(x=data.index, y=lower_band, 
+                             line=dict(color='orange', width=0.7), name='Lower Band'))
+    buy_signals = data[data['Buy']]
+    sell_signals = data[data['Sell']]
+    fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals['Close'], 
+                             mode='markers', marker_symbol='triangle-up',
+                             marker_line_color="green", marker_color="green",
+                             marker_line_width=2, marker_size=10, name='Buy Signal'))
+    fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals['Close'],
+                             mode='markers', marker_symbol='triangle-down',
+                             marker_line_color="red", marker_color="red",
+                             marker_line_width=2, marker_size=10, name='Sell Signal'))
+    fig.update_layout(title='Bali Scalping Strategy Visualization',
+                      xaxis_title='Date',
+                      yaxis_title='Price',
+                      xaxis_rangeslider_visible=False)
+    st.plotly_chart(fig, use_container_width=True)
+