updating backtest wording

app.py

@@ -72,23 +72,27 @@ class TrendFollowingStrategy(bt.Strategy):
 
 
 def fetch_forex_intraday(api_key, from_symbol, to_symbol, interval, outputsize='compact'):
-    url = f'https://www.alphavantage.co/query?function=FX_INTRADAY&from_symbol={from_symbol}&to_symbol={to_symbol}&interval={interval}&apikey={api_key}&outputsize={outputsize}'
-    response = requests.get(url)
-    data = response.json()
+    try:
+        url = f'https://www.alphavantage.co/query?function=FX_INTRADAY&from_symbol={from_symbol}&to_symbol={to_symbol}&interval={interval}&apikey={api_key}&outputsize={outputsize}'
+        response = requests.get(url)
+        data = response.json()
 
-    # Extracting the time series data from the JSON object
-    time_series_key = 'Time Series FX (' + str(interval) + ')'
-    forex_data = pd.DataFrame(data[time_series_key]).T
-    forex_data.columns = ['Open', 'High', 'Low', 'Close']
+        # Extracting the time series data from the JSON object
+        time_series_key = 'Time Series FX (' + str(interval) + ')'
+        forex_data = pd.DataFrame(data[time_series_key]).T
+        forex_data.columns = ['Open', 'High', 'Low', 'Close']
 
-    # Convert index to datetime and sort data
-    forex_data.index = pd.to_datetime(forex_data.index)
-    forex_data.sort_index(inplace=True)
+        # Convert index to datetime and sort data
+        forex_data.index = pd.to_datetime(forex_data.index)
+        forex_data.sort_index(inplace=True)
 
-    # Convert columns to numeric
-    forex_data = forex_data.apply(pd.to_numeric)
+        # Convert columns to numeric
+        forex_data = forex_data.apply(pd.to_numeric)
 
-    return forex_data
+        return forex_data
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+        return None
 
 def analyze_sentiment(json_response, target_ticker):
     """
@@ -232,95 +236,99 @@ def run_backtest(api_key, from_symbol, to_symbol, interval):
     - html_message (str): An HTML message containing the calculated statistics, trade log, and trade decision information.
     """
     # Set up Cerebro engine
-    cerebro = bt.Cerebro()
-    cerebro.addstrategy(TrendFollowingStrategy)
-
-    # Add data feed to Cerebro
-    data = load_data(api_key, from_symbol, to_symbol, interval)
-    cerebro.adddata(data)
-
-    # Set initial cash (optional)
-    cerebro.broker.set_cash(10000)
-
-    # Run the backtest
-    strategy_instance = cerebro.run()[0]
-    api_endpoint = "https://www.alphavantage.co/query"  # Replace with actual endpoint
-
-    # Calculate win and loss percentages
-    total_trades = strategy_instance.trade_count
-    total_wins = strategy_instance.win_count
-    total_losses = strategy_instance.loss_count
-
-    win_percentage = (total_wins / total_trades) * 100
-    loss_percentage = (total_losses / total_trades) * 100
-
-    # Get trade log from the strategy
-    trade_log = strategy_instance.get_trade_log()
-
-    # Iterate through the trade log and count valid trades
-    valid_buy_trades = 0
-    valid_sell_trades = 0
-
-    # Initialize a variable to store the last trade line
-    last_trade_line = ""
-
-    # Iterate through the trade log and count valid trades
-    for trade in trade_log:
-        if trade['trade_type'] == 'CALL':
-            valid_buy_trades += 1
-        elif trade['trade_type'] == 'PUT':
-            valid_sell_trades += 1
-
-        # Store the last trade line
-        last_trade_line = f"Trade {trade['trade_num']}: {trade['trade_type']} - {trade['outcome']}"
-
-    for trade in trade_log:
-        if trade['trade_type'] == 'CALL':
-            valid_buy_trades += 1
-        elif trade['trade_type'] == 'PUT':
-            valid_sell_trades += 1
-
-    # Determine if the backtest agrees (valid Buy trades > valid Sell trades)
-    if win_percentage > loss_percentage:
-        signal = last_trade_line
-        color = "green"
-    else:
-        signal = last_trade_line
-        color = "red"
+    try:
+        cerebro = bt.Cerebro()
+        cerebro.addstrategy(TrendFollowingStrategy)
 
+        # Add data feed to Cerebro
+        data = load_data(api_key, from_symbol, to_symbol, interval)
+        cerebro.adddata(data)
 
-    # Get trade decision information
-    trade_decision, trade_type, trade_timeframe, reason = should_trade(strategy_instance, api_endpoint, api_key, from_symbol, to_symbol)
+        # Set initial cash (optional)
+        cerebro.broker.set_cash(10000)
 
 
-    # Create an HTML message with the calculated statistics, trade log, and trade decision information
-    html_message = f"""
-    <p><strong>Strategy Performance Summary:</strong></p>
-    <p>On the {interval} timeframe</p>
-    <p>*****************************</p>
-    <p>Total Trades: {total_trades}</p>
-    <p>Total Wins: {total_wins} ({win_percentage:.2f}%)</p>
-    <p>Total Losses: {total_losses} ({loss_percentage:.2f}%)</p>
-    <p>Signal: <span style='color: {color}'>{signal}</span></p>
-    <p><strong>Trade Log:</strong></p>
-    <ul>
-    """
-    
-    for trade in trade_log:
-        html_message += f"<li>Trade {trade['trade_num']}: {trade['trade_type']} - {trade['outcome']}</li>"
+        # Run the backtest
+        strategy_instance = cerebro.run()[0]
+        api_endpoint = "https://www.alphavantage.co/query"  # Replace with actual endpoint
 
+        # Calculate win and loss percentages
+        total_trades = strategy_instance.trade_count
+        total_wins = strategy_instance.win_count
+        total_losses = strategy_instance.loss_count
 
-    html_message += "</ul>"
+        win_percentage = (total_wins / total_trades) * 100
+        loss_percentage = (total_losses / total_trades) * 100
 
-    # Include trade decision information
-    html_message += f"""
-    <p><strong>Trade Decision:</strong></p>
-    <p>Trade Type: {trade_type}</p>
-    <p>Timeframe: {trade_timeframe}</p>
-    <p>Reason: {reason}</p>
-    """
+        # Get trade log from the strategy
+        trade_log = strategy_instance.get_trade_log()
+
+        # Iterate through the trade log and count valid trades
+        valid_buy_trades = 0
+        valid_sell_trades = 0
+
+        # Initialize a variable to store the last trade line
+        last_trade_line = ""
+
+        # Iterate through the trade log and count valid trades
+        for trade in trade_log:
+            if trade['trade_type'] == 'CALL':
+                valid_buy_trades += 1
+            elif trade['trade_type'] == 'PUT':
+                valid_sell_trades += 1
+
+            # Store the last trade line
+            last_trade_line = f"Trade {trade['trade_num']}: {trade['trade_type']} - {trade['outcome']}"
+
+        for trade in trade_log:
+            if trade['trade_type'] == 'CALL':
+                valid_buy_trades += 1
+            elif trade['trade_type'] == 'PUT':
+                valid_sell_trades += 1
+
+        # Determine if the backtest agrees (valid Buy trades > valid Sell trades)
+        if win_percentage > loss_percentage:
+            signal = last_trade_line
+            color = "green"
+        else:
+            signal = last_trade_line
+            color = "red"
+
+
+        # Get trade decision information
+        trade_decision, trade_type, trade_timeframe, reason = should_trade(strategy_instance, api_endpoint, api_key, from_symbol, to_symbol)
+
+
+        # Create an HTML message with the calculated statistics, trade log, and trade decision information
+        html_message = f"""
+        <p><strong>Strategy Performance Summary:</strong></p>
+        <p>On the {interval} timeframe</p>
+        <p>*****************************</p>
+        <p>Total Trades: {total_trades}</p>
+        <p>Total Wins: {total_wins} ({win_percentage:.2f}%)</p>
+        <p>Total Losses: {total_losses} ({loss_percentage:.2f}%)</p>
+        <p>Signal: <span style='color: {color}'>{signal}</span></p>
+        <p><strong>Trade Log:</strong></p>
+        <ul>
+        """
+        
+        for trade in trade_log:
+            html_message += f"<li>Trade {trade['trade_num']}: {trade['trade_type']} - {trade['outcome']}</li>"
+
+
+        html_message += "</ul>"
+
+        # Include trade decision information
+        html_message += f"""
+        <p><strong>Trade Decision:</strong></p>
+        <p>Trade Type: {trade_type}</p>
+        <p>Timeframe: {trade_timeframe}</p>
+        <p>Reason: {reason}</p>
+        """
 
-    return html_message
+        return html_message
+    except Exception as e:
+        return f"Waiting for data"
 
 
 # Define a list of popular currency pairs for the dropdowns