Update app.py

app.py

@@ -1,84 +1,16 @@
-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
-
-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.
-""")
-
-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)
-
-@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 manually
-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 manually
-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 = []
+# ... (previous code remains the same)
 
 # Backtesting simulation loop
 for i in range(1, len(data)):
     today = data.index[i]
-    price = data['Close'].iloc[i]  # Ensure price is a scalar value
-    rsi_today = data['RSI'].iloc[i]  # Ensure RSI is a scalar value
+    price = float(data['Close'].iloc[i])  # Cast to float
+    rsi_today = float(data['RSI'].iloc[i])  # Cast to float
     
     # Check for buy signal
-    macd_today = data['MACD'].iloc[i]
-    signal_today = data['MACD_signal'].iloc[i]
-    macd_yesterday = data['MACD'].iloc[i-1]
-    signal_yesterday = data['MACD_signal'].iloc[i-1]
+    macd_today = float(data['MACD'].iloc[i])
+    signal_today = float(data['MACD_signal'].iloc[i])
+    macd_yesterday = float(data['MACD'].iloc[i-1])
+    signal_yesterday = float(data['MACD_signal'].iloc[i-1])
     
     buy_condition = (macd_yesterday < signal_yesterday) and (macd_today > signal_today) and (rsi_today < 50)
     
@@ -93,7 +25,7 @@ for i in range(1, len(data)):
                 "entry_price": price,
                 "allocated": allocation,
                 "shares": shares_bought,
-                "highest": price,
+                "highest": price,  # Ensure scalar
                 "trailing_stop": price * (1 - trailing_stop_pct)
             })
             data.at[today, 'Buy'] = True
@@ -101,12 +33,13 @@ for i in range(1, len(data)):
     # Update positions and check sell conditions
     positions_to_remove = []
     for idx, pos in enumerate(open_positions):
-        if price > pos["highest"]:
+        current_highest = pos["highest"]
+        if price > current_highest:
             pos["highest"] = price
             pos["trailing_stop"] = pos["highest"] * (1 - trailing_stop_pct)
         
         # Partial sell condition
-        if price >= pos["entry_price"] * target_multiplier and rsi_today > 50:
+        if price >= (pos["entry_price"] * target_multiplier) and rsi_today > 50:
             shares_to_sell = pos["shares"] * sell_fraction
             cash += shares_to_sell * price
             pos["shares"] -= shares_to_sell
@@ -124,7 +57,8 @@ for i in range(1, len(data)):
             continue
         
         # Trailing stop exit
-        if price < pos["trailing_stop"]:
+        current_trailing_stop = pos["trailing_stop"]
+        if price < current_trailing_stop:
             cash += pos["shares"] * price
             completed_trades.append({
                 "entry_date": pos["entry_date"],
@@ -142,39 +76,4 @@ for i in range(1, len(data)):
     position_value = sum(pos["shares"] * price for pos in open_positions)
     equity_curve.append(cash + position_value)
 
-# 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.*
-""")
+# ... (remaining code remains the same)