Update app.py

app.py

@@ -1,10 +1,81 @@
-# ... (previous code remains the same)
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from datetime import timedelta
+
+# Streamlit App Title and Description
+st.title("Extended MACD-RSI Combo Strategy for SPY")
+st.markdown("""
+This app demonstrates an extended MACD-RSI based trading strategy on SPY with the following features:
+- **Multiple Simultaneous Positions:** Each buy signal creates a new position.
+- **Dynamic Trailing Stop:** Each open position is updated with a trailing stop.
+- **Configurable Parameters:** Adjust strategy parameters via the sidebar.
+- **Buy Rule:**  
+  Buy a fraction of available cash when:
+  - The MACD line crosses above its signal line.
+  - RSI is below 50.
+  - No buy has been executed in the last few days.
+- **Sell Rule:**  
+  For each position:
+  - **Partial Sell:** Sell a fraction of the position when the price reaches a target multiple of the entry price and RSI is above 50.
+  - **Trailing Stop Exit:** If the price falls below the position’s dynamic trailing stop, sell the entire position.
+""")
+
+# Sidebar for Strategy Parameters
+st.sidebar.header("Strategy Parameters")
+buy_fraction = st.sidebar.slider("Buy Fraction (of available cash)", 0.05, 0.50, 0.15, 0.05)
+sell_fraction = st.sidebar.slider("Partial Sell Fraction", 0.10, 0.90, 0.40, 0.05)
+target_multiplier = st.sidebar.slider("Target Multiplier", 1.01, 1.20, 1.08, 0.01)
+trailing_stop_pct = st.sidebar.slider("Trailing Stop (%)", 0.01, 0.20, 0.08, 0.01)
+min_days_between_buys = st.sidebar.number_input("Minimum Days Between Buys", min_value=1, max_value=10, value=2)
+
+# Load SPY Data
+@st.cache_data
+def load_data(ticker, period="1y"):
+    data = yf.download(ticker, period=period)
+    data.dropna(inplace=True)
+    return data
+
+data_load_state = st.text("Loading SPY data...")
+data = load_data("SPY", period="1y")
+data_load_state.text("Loading SPY data...done!")
+
+# --- Manual Calculation of Technical Indicators ---
+
+# Calculate MACD
+data['EMA12'] = data['Close'].ewm(span=12, adjust=False).mean()
+data['EMA26'] = data['Close'].ewm(span=26, adjust=False).mean()
+data['MACD'] = data['EMA12'] - data['EMA26']
+data['MACD_signal'] = data['MACD'].ewm(span=9, adjust=False).mean()
+
+# Calculate RSI
+delta = data['Close'].diff()
+gain = delta.where(delta > 0, 0)
+loss = -delta.where(delta < 0, 0)
+avg_gain = gain.rolling(window=14).mean()
+avg_loss = loss.rolling(window=14).mean()
+rs = avg_gain / avg_loss
+data['RSI'] = 100 - (100 / (1 + rs))
+
+# Initialize signal flags for plotting
+data['Buy'] = False
+data['Sell'] = False
+
+# Backtesting parameters
+initial_capital = 100000
+cash = initial_capital
+equity_curve = []
+last_buy_date = None
+open_positions = []
+completed_trades = []
 
 # Backtesting simulation loop
 for i in range(1, len(data)):
     today = data.index[i]
-    price = float(data['Close'].iloc[i])  # Cast to float
-    rsi_today = float(data['RSI'].iloc[i])  # Cast to float
+    price = float(data['Close'].iloc[i])  # Ensure price is a scalar value
+    rsi_today = float(data['RSI'].iloc[i])  # Ensure RSI is a scalar value
     
     # Check for buy signal
     macd_today = float(data['MACD'].iloc[i])
@@ -25,7 +96,7 @@ for i in range(1, len(data)):
                 "entry_price": price,
                 "allocated": allocation,
                 "shares": shares_bought,
-                "highest": price,  # Ensure scalar
+                "highest": price,
                 "trailing_stop": price * (1 - trailing_stop_pct)
             })
             data.at[today, 'Buy'] = True
@@ -76,4 +147,39 @@ for i in range(1, len(data)):
     position_value = sum(pos["shares"] * price for pos in open_positions)
     equity_curve.append(cash + position_value)
 
-# ... (remaining code remains the same)
+# Build performance DataFrame
+performance = pd.DataFrame({
+    'Date': data.index[1:len(equity_curve)+1],
+    'Equity': equity_curve
+}).set_index('Date')
+
+# Plot results
+st.subheader("Equity Curve")
+fig, ax = plt.subplots(figsize=(10, 4))
+ax.plot(performance.index, performance['Equity'], label="Total Equity")
+ax.set_xlabel("Date")
+ax.set_ylabel("Equity ($)")
+ax.legend()
+st.pyplot(fig)
+
+st.subheader("SPY Price with Buy/Sell Signals")
+fig2, ax2 = plt.subplots(figsize=(10, 4))
+ax2.plot(data.index, data['Close'], label="SPY Close Price", color='black')
+ax2.scatter(data.index[data['Buy']], data['Close'][data['Buy']], marker="^", color="green", label="Buy Signal", s=100)
+ax2.scatter(data.index[data['Sell']], data['Close'][data['Sell']], marker="v", color="red", label="Sell Signal", s=100)
+ax2.set_xlabel("Date")
+ax2.set_ylabel("Price ($)")
+ax2.legend()
+st.pyplot(fig2)
+
+# Display performance metrics
+final_equity = equity_curve[-1]
+return_pct = ((final_equity - initial_capital) / initial_capital) * 100
+st.subheader("Strategy Performance Metrics")
+st.write(f"**Initial Capital:** ${initial_capital:,.2f}")
+st.write(f"**Final Equity:** ${final_equity:,.2f}")
+st.write(f"**Return:** {return_pct:.2f}%")
+
+st.markdown("""
+*This extended demo is for educational purposes only and does not constitute financial advice. Always test your strategies extensively before trading with real money.*
+""")