src/StrategyInstance.py

@@ -90,7 +90,6 @@ class StrategyInstance:
             'set_exit': self.set_exit,
             'set_available_strategy_balance': self.set_available_strategy_balance,
             'get_current_balance': self.get_current_balance,
-            'get_available_balance': self.get_available_balance,
             'get_available_strategy_balance': self.get_available_strategy_balance,
             'starting_balance': self.starting_balance,
             'current_balance': self.current_balance,
@@ -374,13 +373,7 @@ class StrategyInstance:
 
             # Call the 'next()' method if defined
             if 'next' in self.exec_context and callable(self.exec_context['next']):
-                # Log flags before execution for debugging
-                logger.debug(f"[STRATEGY EXEC] Flags before next(): {self.flags}")
-                logger.debug(f"[STRATEGY EXEC] Variables before next(): {self.variables}")
                 self.exec_context['next']()
-                # Log flags after execution for debugging
-                logger.debug(f"[STRATEGY EXEC] Flags after next(): {self.flags}")
-                logger.debug(f"[STRATEGY EXEC] Variables after next(): {self.variables}")
             else:
                 logger.error(
                     f"'next' method not defined in generated_code for StrategyInstance '{self.strategy_instance_id}'."

src/backtest_strategy_instance.py

@@ -49,9 +49,6 @@ class BacktestStrategyInstance(StrategyInstance):
         # Initialize last_valid_values for indicators
         self.last_valid_values={}
 
-        # Initialize collected alerts for backtest results
-        self.collected_alerts = []
-
     def set_backtrader_strategy(self, backtrader_strategy: bt.Strategy):
         """
         Sets the backtrader_strategy and initializes broker-dependent attributes.
@@ -150,15 +147,14 @@ class BacktestStrategyInstance(StrategyInstance):
         If no last valid value exists, searches forward for the next valid value.
         If no valid value is found, returns a default value (e.g., 1).
         """
-        logger.info(f"[BACKTEST] process_indicator called: indicator='{indicator_name}', output='{output_field}'")
+        logger.debug(f"Backtester is retrieving indicator '{indicator_name}' from precomputed data.")
         if self.backtrader_strategy is None:
             logger.error("Backtrader strategy is not set in StrategyInstance.")
             return None
 
         df = self.backtrader_strategy.precomputed_indicators.get(indicator_name)
         if df is None:
-            logger.error(f"[BACKTEST DEBUG] Indicator '{indicator_name}' not found in precomputed_indicators!")
+            logger.error(f"Indicator '{indicator_name}' not found.")
-            logger.error(f"[BACKTEST DEBUG] Available indicators: {list(self.backtrader_strategy.precomputed_indicators.keys())}")
             return None
 
         idx = self.backtrader_strategy.indicator_pointers.get(indicator_name, 0)
@@ -168,19 +164,6 @@ class BacktestStrategyInstance(StrategyInstance):
 
         # Retrieve the value at the current index
         value = df.iloc[idx].get(output_field)
-        indicator_time = df.iloc[idx].get('time', 'N/A')
-
-        # Get current candle time for comparison
-        candle_time = None
-        if self.backtrader_strategy and self.backtrader_strategy.data:
-            try:
-                candle_time = self.backtrader_strategy.data.datetime.datetime(0)
-            except:
-                pass
-
-        # Log indicator values for debugging (first 10 and every 50th)
-        if idx < 10 or idx % 50 == 0:
-            logger.info(f"[BACKTEST] process_indicator('{indicator_name}', '{output_field}') at idx={idx}: value={value}, indicator_time={indicator_time}, candle_time={candle_time}")
 
         if pd.isna(value):
             # Check if we have a cached last valid value
@@ -212,10 +195,6 @@ class BacktestStrategyInstance(StrategyInstance):
             if indicator_name not in self.last_valid_values:
                 self.last_valid_values[indicator_name] = {}
             self.last_valid_values[indicator_name][output_field] = value
-            # Log the returned value for debugging
-            idx = self.backtrader_strategy.indicator_pointers.get(indicator_name, 0)
-            if idx < 10 or idx % 50 == 0:
-                logger.info(f"[BACKTEST] process_indicator returning: {indicator_name}.{output_field} = {value}")
             return value
 
     # 3. Override get_current_price
@@ -324,36 +303,10 @@ class BacktestStrategyInstance(StrategyInstance):
     # 9. Override notify_user
     def notify_user(self, message: str):
         """
-        Collects notifications with timestamps for display in backtest results.
+        Suppresses user notifications and instead logs them.
         :param message: Notification message.
         """
-        timestamp = self.get_current_candle_datetime()
+        logger.debug(f"Backtest notification: {message}")
-        alert = {
-            # Append 'Z' to indicate UTC timezone (Backtrader uses UTC internally)
-            'timestamp': (timestamp.isoformat() + 'Z') if timestamp else None,
-            'message': message
-        }
-        self.collected_alerts.append(alert)
-        logger.debug(f"Backtest notification: {message} (at {timestamp})")
-
-    def get_current_candle_datetime(self) -> dt.datetime:
-        """
-        Gets the datetime of the current candle from backtrader's data feed.
-        """
-        if self.backtrader_strategy is None:
-            return dt.datetime.now()
-        try:
-            # Use the data feed's datetime method to get proper datetime
-            return self.backtrader_strategy.data.datetime.datetime(0)
-        except Exception as e:
-            logger.warning(f"Could not get candle datetime: {e}")
-            return dt.datetime.now()
-
-    def get_collected_alerts(self) -> list:
-        """
-        Returns the list of collected alerts for inclusion in backtest results.
-        """
-        return self.collected_alerts
 
     def save_context(self):
         """

src/backtesting.py

@@ -323,23 +323,11 @@ class Backtester:
 
     def precompute_indicators(self, indicators_definitions: list, user_name: str, data_feed: pd.DataFrame) -> dict:
         """
-        Precompute indicator values directly on the backtest data feed.
+        Precompute indicator values and return a dictionary of DataFrames.
-
-        IMPORTANT: This computes indicators on the actual backtest candle data,
-        ensuring the indicator values align with the price data used in the backtest.
-        Previously, this fetched fresh/latest candles which caused misalignment.
         """
-        import json as json_module  # Local import to avoid conflicts
-
         precomputed_indicators = {}
         total_candles = len(data_feed)
 
-        logger.info(f"[BACKTEST] precompute_indicators called with {len(indicators_definitions)} indicator definitions")
-        logger.info(f"[BACKTEST] user_name: {user_name}, total_candles: {total_candles}")
-        logger.info(f"[BACKTEST] data_feed columns: {list(data_feed.columns)}")
-        logger.info(f"[BACKTEST] data_feed first row: {data_feed.iloc[0].to_dict() if len(data_feed) > 0 else 'empty'}")
-        logger.info(f"[BACKTEST] data_feed last row: {data_feed.iloc[-1].to_dict() if len(data_feed) > 0 else 'empty'}")
-
         # Aggregate requested outputs for each indicator
         indicator_outputs = {}
         for indicator_def in indicators_definitions:
@@ -362,142 +350,47 @@ class Backtester:
                 # If output is None, we need all outputs
                 indicator_outputs[indicator_name] = None  # None indicates all outputs
 
-        # Get user ID for indicator lookup
+        # Now, precompute each unique indicator with the required outputs
-        user_id = self.data_cache.get_datacache_item(
-            item_name='id',
-            cache_name='users',
-            filter_vals=('user_name', user_name)
-        )
-
-        logger.info(f"[BACKTEST] indicator_outputs to precompute: {indicator_outputs}")
-
-        # Prepare candle data for indicator calculation
-        # Convert data_feed to the format expected by indicators
-        candle_data = data_feed.copy()
-
-        # Ensure required columns exist
-        if 'time' not in candle_data.columns and candle_data.index.name == 'datetime':
-            candle_data['time'] = candle_data.index.astype(np.int64) // 10**6  # Convert to milliseconds
-
         for indicator_name, outputs in indicator_outputs.items():
-            logger.info(f"[BACKTEST] Computing indicator '{indicator_name}' on backtest data feed")
+            # Compute the indicator values
-
+            indicator_data = self.indicators_manager.get_latest_indicator_data(
-            try:
+                user_name=user_name,
-                # Fetch indicator definition from cache
+                indicator_name=indicator_name,
-                indicators = self.data_cache.get_rows_from_datacache(
+                num_results=total_candles
-                    cache_name='indicators',
-                    filter_vals=[('creator', str(user_id)), ('name', indicator_name)]
             )
 
-                if indicators.empty:
+            if not indicator_data:
-                    logger.warning(f"[BACKTEST] Indicator '{indicator_name}' not found for user '{user_name}' (id={user_id}). Skipping.")
+                logger.warning(f"No data returned for indicator '{indicator_name}'. Skipping.")
                 continue
 
-                indicator = indicators.iloc[0]
+            data = indicator_data.get(indicator_name)
-                kind = indicator['kind']
-                properties = json_module.loads(indicator['properties']) if isinstance(indicator['properties'], str) else indicator['properties']
 
-                logger.info(f"[BACKTEST] Indicator '{indicator_name}' is of kind '{kind}' with properties: {properties}")
+            # Convert the data to a DataFrame
-
+            if isinstance(data, list):
-                # Get the indicator class from the registry
+                df = pd.DataFrame(data)
-                indicator_class = self.indicators_manager.indicator_registry.get(kind)
+            elif isinstance(data, dict):
-                if not indicator_class:
+                df = pd.DataFrame([data])
-                    logger.warning(f"[BACKTEST] Unknown indicator kind '{kind}' for '{indicator_name}'. Skipping.")
+            else:
+                logger.warning(f"Unexpected data format for indicator '{indicator_name}'. Skipping.")
                 continue
 
-                # Instantiate and calculate
-                indicator_obj = indicator_class(name=indicator_name, indicator_type=kind, properties=properties)
-                result_df = indicator_obj.calculate(candles=candle_data, user_name=user_name, num_results=total_candles)
-
-                if result_df is None or (isinstance(result_df, pd.DataFrame) and result_df.empty):
-                    logger.warning(f"[BACKTEST] No data computed for indicator '{indicator_name}'. Skipping.")
-                    continue
-
-                logger.info(f"[BACKTEST] Computed indicator '{indicator_name}': {len(result_df)} rows, columns: {list(result_df.columns)}")
-
-                # Log first few values for debugging
-                if len(result_df) > 0:
-                    logger.info(f"[BACKTEST] First 3 rows of '{indicator_name}': {result_df.head(3).to_dict('records')}")
-                    logger.info(f"[BACKTEST] Last 3 rows of '{indicator_name}': {result_df.tail(3).to_dict('records')}")
-
             # If outputs is None, keep all outputs
             if outputs is not None:
                 # Include 'time' and requested outputs
                 columns_to_keep = ['time'] + list(outputs)
-                    missing_columns = [col for col in columns_to_keep if col not in result_df.columns]
+                missing_columns = [col for col in columns_to_keep if col not in df.columns]
                 if missing_columns:
-                        logger.warning(f"[BACKTEST] Indicator '{indicator_name}' missing columns: {missing_columns}. Available: {list(result_df.columns)}")
+                    logger.warning(f"Indicator '{indicator_name}' missing columns: {missing_columns}. Skipping.")
-                        # Try to continue with available columns
+                    continue
-                        columns_to_keep = [c for c in columns_to_keep if c in result_df.columns]
+                df = df[columns_to_keep]
-                    result_df = result_df[columns_to_keep]
 
             # Reset index and store the DataFrame
-                result_df.reset_index(drop=True, inplace=True)
+            df.reset_index(drop=True, inplace=True)
-                precomputed_indicators[indicator_name] = result_df
+            precomputed_indicators[indicator_name] = df
-                logger.info(f"[BACKTEST] Precomputed indicator '{indicator_name}' with {len(result_df)} data points.")
+            logger.debug(f"Precomputed indicator '{indicator_name}' with {len(df)} data points.")
-
-            except Exception as e:
-                logger.error(f"[BACKTEST] Error computing indicator '{indicator_name}': {e}", exc_info=True)
-                continue
 
         return precomputed_indicators
 
-    def _calculate_warmup_period(self, indicators_definitions: list, user_name: str) -> int:
-        """
-        Calculate the maximum warmup period needed based on indicator periods.
-
-        :param indicators_definitions: List of indicator definitions from strategy
-        :param user_name: Username for looking up indicator configs
-        :return: Maximum warmup period in candles
-        """
-        import json as json_module
-
-        max_period = 0
-
-        user_id = self.data_cache.get_datacache_item(
-            item_name='id',
-            cache_name='users',
-            filter_vals=('user_name', user_name)
-        )
-
-        for indicator_def in indicators_definitions:
-            indicator_name = indicator_def.get('name')
-            if not indicator_name:
-                continue
-
-            try:
-                indicators = self.data_cache.get_rows_from_datacache(
-                    cache_name='indicators',
-                    filter_vals=[('creator', str(user_id)), ('name', indicator_name)]
-                )
-
-                if indicators.empty:
-                    continue
-
-                indicator = indicators.iloc[0]
-                properties = json_module.loads(indicator['properties']) if isinstance(indicator['properties'], str) else indicator['properties']
-
-                # Get period from properties (most indicators use 'period')
-                period = properties.get('period', 0)
-                if period > max_period:
-                    max_period = period
-
-            except Exception as e:
-                logger.warning(f"Could not get period for indicator '{indicator_name}': {e}")
-                continue
-
-        logger.info(f"[BACKTEST] Maximum indicator warmup period: {max_period}")
-        return max_period
-
-    def _get_timeframe_minutes(self, timeframe: str) -> int:
-        """Convert timeframe string to minutes."""
-        timeframe_map = {
-            '1m': 1, '3m': 3, '5m': 5, '15m': 15, '30m': 30,
-            '1h': 60, '2h': 120, '4h': 240, '6h': 360, '12h': 720,
-            '1d': 1440, '1w': 10080
-        }
-        return timeframe_map.get(timeframe.lower(), 60)  # Default to 1h
-
     def prepare_backtest_data(self, msg_data: dict, strategy_components: dict) -> tuple:
         """
         Prepare the data feed and precomputed indicators for backtesting.
@@ -522,71 +415,19 @@ class Backtester:
         main_source = data_sources[0]
         logger.info(f"Using main_source for backtest: {main_source}")
 
-        # Calculate warmup period needed for indicators
+        # Prepare the main data feed
-        indicators_definitions = strategy_components.get('indicators', [])
+        start_date = msg_data.get('start_date', '2023-01-01T00:00')
-        warmup_candles = self._calculate_warmup_period(indicators_definitions, user_name)
+        logger.info(f"Backtest start_date: {start_date}")
-
+        data_feed = self.prepare_data_feed(start_date, main_source, user_name)
-        # Get timeframe to calculate how far back to fetch for warmup
-        timeframe = main_source.get('timeframe', '1h')
-        timeframe_minutes = self._get_timeframe_minutes(timeframe)
-        warmup_minutes = warmup_candles * timeframe_minutes
-
-        # Prepare the main data feed with extended start for warmup
-        original_start_date = msg_data.get('start_date', '2023-01-01T00:00')
-        logger.info(f"Backtest original start_date: {original_start_date}")
-
-        # Calculate adjusted start date for indicator warmup
-        original_start_dt = dt.datetime.strptime(original_start_date, '%Y-%m-%dT%H:%M')
-        adjusted_start_dt = original_start_dt - dt.timedelta(minutes=warmup_minutes)
-        adjusted_start_date = adjusted_start_dt.strftime('%Y-%m-%dT%H:%M')
-
-        logger.info(f"[BACKTEST] Fetching data from {adjusted_start_date} (adjusted for {warmup_candles} warmup candles)")
-
-        data_feed = self.prepare_data_feed(adjusted_start_date, main_source, user_name)
 
         if data_feed.empty:
             logger.error("Data feed could not be prepared. Please check the data source.")
             raise ValueError("Data feed could not be prepared. Please check the data source.")
 
-        # Precompute indicator values on the full dataset (including warmup candles)
+        # Precompute indicator values
+        indicators_definitions = strategy_components.get('indicators', [])
         precomputed_indicators = self.precompute_indicators(indicators_definitions, user_name, data_feed)
 
-        # Now trim BOTH the data feed AND indicators to start at the user's original start_date
-        # This ensures the first indicator values in the backtest have full warmup context
-        if precomputed_indicators:
-            # Find where the original start_date falls in the data
-            original_start_unix = original_start_dt.replace(tzinfo=dt.timezone.utc).timestamp()
-
-            # Find the index where we should start the backtest
-            backtest_start_idx = 0
-            for idx, row in data_feed.iterrows():
-                if row['time'] >= original_start_unix:
-                    backtest_start_idx = idx
-                    break
-
-            # Calculate how many indicator rows to skip
-            min_indicator_len = min(len(df) for df in precomputed_indicators.values())
-            original_feed_len = len(data_feed)
-            indicator_warmup = original_feed_len - min_indicator_len
-
-            # The effective backtest start is max of (user's start, indicator warmup)
-            effective_start_idx = max(backtest_start_idx, indicator_warmup)
-
-            logger.info(f"[BACKTEST] Original data length: {original_feed_len}, indicator warmup: {indicator_warmup}, user start idx: {backtest_start_idx}, effective start: {effective_start_idx}")
-
-            # Trim data feed
-            if effective_start_idx > 0:
-                data_feed = data_feed.iloc[effective_start_idx:].reset_index(drop=True)
-                logger.info(f"[BACKTEST] Trimmed data feed to {len(data_feed)} rows starting from effective start")
-
-            # Trim indicators to match
-            # Indicators already have warmup_period fewer rows, so we need to adjust
-            indicator_trim = effective_start_idx - indicator_warmup
-            if indicator_trim > 0:
-                for name, df in precomputed_indicators.items():
-                    precomputed_indicators[name] = df.iloc[indicator_trim:].reset_index(drop=True)
-                    logger.info(f"[BACKTEST] Trimmed indicator '{name}' to {len(precomputed_indicators[name])} rows")
-
         logger.info("Backtest data prepared successfully.")
 
         return data_feed, precomputed_indicators
@@ -605,16 +446,7 @@ class Backtester:
             try:
                 # **Convert 'time' to 'datetime' if necessary**
                 if 'time' in data_feed.columns:
-                    # Time values from EDM are Unix timestamps in SECONDS, not milliseconds
+                    data_feed['datetime'] = pd.to_datetime(data_feed['time'], unit='ms')
-                    data_feed['datetime'] = pd.to_datetime(data_feed['time'], unit='s')
-                    # DEBUG: Log first and last timestamps to verify conversion
-                    if len(data_feed) > 0:
-                        first_time = data_feed['time'].iloc[0]
-                        last_time = data_feed['time'].iloc[-1]
-                        first_dt = data_feed['datetime'].iloc[0]
-                        last_dt = data_feed['datetime'].iloc[-1]
-                        logger.info(f"[DEBUG DATETIME FIX] First raw time: {first_time}, converted: {first_dt}")
-                        logger.info(f"[DEBUG DATETIME FIX] Last raw time: {last_time}, converted: {last_dt}")
                     data_feed.set_index('datetime', inplace=True)
                     logger.info("Converted 'time' to 'datetime' and set as index in data_feed.")
 
@@ -677,12 +509,6 @@ class Backtester:
                                                          'progress': 100}}
                                    , room=socket_conn_id)
 
-                # Get collected alerts from strategy instance
-                collected_alerts = strategy_instance.get_collected_alerts()
-
-                # Get trading source info for chart validation
-                trading_source = msg_data.get('trading_source', {})
-
                 # Prepare the results to pass into the callback
                 backtest_results = {
                     "success": True,  # Indicate success
@@ -691,8 +517,6 @@ class Backtester:
                     "run_duration": run_duration,
                     "equity_curve": equity_curve,
                     "trades": trades,
-                    "alerts": collected_alerts,
-                    "trading_source": trading_source,
                 }
 
                 logger.info("Backtest executed successfully.")
@@ -773,8 +597,6 @@ class Backtester:
             }
             logger.info(f"Using default_source for backtest data: {source}")
             strategy_components['data_sources'] = [source]
-            # Store trading source in msg_data for inclusion in backtest results
-            msg_data['trading_source'] = source
 
         try:
             data_feed, precomputed_indicators = self.prepare_backtest_data(msg_data, strategy_components)

src/mapped_strategy.py

@@ -87,14 +87,9 @@ class MappedStrategy(bt.Strategy):
             f"notify_trade called for trade {trade.ref}, PnL: {trade.pnl}, Status: {trade.status_names[trade.status]}")
 
         if trade.isopen:
-            # Trade just opened - use current bar's datetime from data feed
+            # Trade just opened
-            current_dt = self.data.datetime.datetime(0)
-            # Append 'Z' to indicate UTC timezone (Backtrader uses UTC internally)
-            open_datetime = (current_dt.isoformat() + 'Z') if current_dt else None
-            # Debug logging
-            raw_dt = self.data.datetime[0]
-            logger.info(f"[DEBUG] Trade open - raw datetime[0]={raw_dt}, converted={current_dt}, iso={open_datetime}")
             self.log(f"TRADE OPENED, Size: {trade.size}, Price: {trade.price}")
+            open_datetime = bt.num2date(trade.dtopen).isoformat() if trade.dtopen else None
             trade_info = {
                 'ref': trade.ref,
                 'size': trade.size,
@@ -104,11 +99,9 @@ class MappedStrategy(bt.Strategy):
             # Store the trade_info with trade.ref as key
             self.open_trades[trade.ref] = trade_info
         elif trade.isclosed:
-            # Trade just closed - use current bar's datetime from data feed
+            # Trade just closed
-            current_dt = self.data.datetime.datetime(0)
-            # Append 'Z' to indicate UTC timezone (Backtrader uses UTC internally)
-            close_datetime = (current_dt.isoformat() + 'Z') if current_dt else None
             self.log(f"TRADE CLOSED, GROSS P/L: {trade.pnl}, NET P/L: {trade.pnlcomm}")
+            close_datetime = bt.num2date(trade.dtclose).isoformat() if trade.dtclose else None
             # Retrieve open trade details
             trade_info = self.open_trades.pop(trade.ref, {})
             # Get the close price from data feed
@@ -136,28 +129,9 @@ class MappedStrategy(bt.Strategy):
 
         self.current_step += 1
 
-        # Debug: Log current price and indicator values every N steps
-        if self.current_step <= 10 or self.current_step % 50 == 0:
-            current_price = self.data.close[0]
-            logger.info(f"[BACKTEST STEP {self.current_step}] Price: {current_price:.2f}")
-
-            # Log all indicator values at this step
-            for ind_name, df in self.precomputed_indicators.items():
-                idx = self.indicator_pointers.get(ind_name, 0)
-                if idx < len(df):
-                    row = df.iloc[idx]
-                    # Log all columns except 'time'
-                    values = {col: row[col] for col in df.columns if col != 'time'}
-                    logger.info(f"[BACKTEST STEP {self.current_step}] Indicator '{ind_name}' at idx {idx}: {values}")
-
         # Execute the strategy logic
         self.execute_strategy()
 
-        # Advance indicator pointers for the next candle
-        for name in self.indicator_names:
-            if name in self.indicator_pointers:
-                self.indicator_pointers[name] += 1
-
         # Check if we're at the second-to-last bar
         if self.current_step == (self.p.data_length - 1):
             if self.position:

src/static/backtesting.js

@@ -207,9 +207,6 @@ class Backtesting {
         this.setText(this.progressBar, '0%');
         this.resultsDisplay.innerHTML = ''; // Clear previous results
         this.displayMessage('Backtest started...', 'blue');
-
-        // Clear previous trade markers from chart
-        this.clearTradeMarkers();
     }
 
     displayTestResults(results) {
@@ -256,8 +253,6 @@ class Backtesting {
                             <thead>
                                 <tr>
                                     <th>Trade ID</th>
-                                    <th>Open Time</th>
-                                    <th>Close Time</th>
                                     <th>Size</th>
                                     <th>Open Price</th>
                                     <th>Close Price</th>
@@ -271,14 +266,10 @@ class Backtesting {
                     const openPrice = trade.open_price != null ? trade.open_price.toFixed(2) : 'N/A';
                     const closePrice = trade.close_price != null ? trade.close_price.toFixed(2) : 'N/A';
                     const pnl = trade.pnl != null ? trade.pnl.toFixed(2) : 'N/A';
-                    const openTime = trade.open_datetime ? this.formatTradeDateTime(trade.open_datetime) : 'N/A';
-                    const closeTime = trade.close_datetime ? this.formatTradeDateTime(trade.close_datetime) : 'N/A';
 
                     html += `
                         <tr>
                             <td>${trade.ref}</td>
-                            <td>${openTime}</td>
-                            <td>${closeTime}</td>
                             <td>${size}</td>
                             <td>${openPrice}</td>
                             <td>${closePrice}</td>
@@ -286,9 +277,6 @@ class Backtesting {
                         </tr>
                     `;
                 });
-
-                // Automatically show trade markers on chart
-                this.showTradeMarkersOnChart(results.trades, results.trading_source);
                 html += `
                             </tbody>
                         </table>
@@ -299,36 +287,6 @@ class Backtesting {
             html += `<p>No trades were executed.</p>`;
         }
 
-        // Strategy Alerts Section
-        if (results.alerts && results.alerts.length > 0) {
-            html += `
-                <h4>Strategy Alerts</h4>
-                <div style="max-height: 200px; overflow-y: auto;">
-                    <table border="1" cellpadding="5" cellspacing="0">
-                        <thead>
-                            <tr>
-                                <th>Timestamp</th>
-                                <th>Message</th>
-                            </tr>
-                        </thead>
-                        <tbody>
-            `;
-            results.alerts.forEach(alert => {
-                const timestamp = alert.timestamp ? this.formatTradeDateTime(alert.timestamp) : 'N/A';
-                html += `
-                    <tr>
-                        <td>${timestamp}</td>
-                        <td>${alert.message || ''}</td>
-                    </tr>
-                `;
-            });
-            html += `
-                        </tbody>
-                    </table>
-                </div>
-            `;
-        }
-
         this.resultsDisplay.innerHTML = html;
         this.drawEquityCurveChart(results.equity_curve);
     }
@@ -586,9 +544,6 @@ class Backtesting {
         this.hideElement(this.resultsContainer);
         this.hideElement(this.progressContainer);
         this.clearMessage();
-
-        // Clear trade markers from chart
-        this.clearTradeMarkers();
     }
 
     clearForm() {
@@ -686,65 +641,5 @@ class Backtesting {
         return `${year}-${month}-${day}T${hours}:${minutes}`;
     }
 
-    // Format trade datetime for display in trades table (shows both local and UTC)
-    formatTradeDateTime(dateTimeStr) {
-        if (!dateTimeStr) return 'N/A';
-        try {
-            const date = new Date(dateTimeStr);
-            const pad = (num) => num.toString().padStart(2, '0');
-
-            // Local time
-            const localMonth = pad(date.getMonth() + 1);
-            const localDay = pad(date.getDate());
-            const localHours = pad(date.getHours());
-            const localMinutes = pad(date.getMinutes());
-            const localStr = `${localMonth}/${localDay} ${localHours}:${localMinutes}`;
-
-            // UTC time
-            const utcMonth = pad(date.getUTCMonth() + 1);
-            const utcDay = pad(date.getUTCDate());
-            const utcHours = pad(date.getUTCHours());
-            const utcMinutes = pad(date.getUTCMinutes());
-            const utcStr = `${utcMonth}/${utcDay} ${utcHours}:${utcMinutes}`;
-
-            return `${localStr} (UTC: ${utcStr})`;
-        } catch (e) {
-            return dateTimeStr;
-        }
-    }
-
-    // Show all trade markers on chart for the backtest results
-    showTradeMarkersOnChart(trades, tradingSource) {
-        // Check if charts are available
-        if (!this.ui.charts) {
-            console.log('Charts not available, skipping trade markers');
-            return;
-        }
-
-        // Validate that the current chart matches the backtest's trading source
-        const normalizeSymbol = (s) => (s || '').toUpperCase().replace(/[\/\-]/g, '');
-        const currentNormalized = normalizeSymbol(this.ui.charts.trading_pair);
-        const backtestNormalized = normalizeSymbol(tradingSource?.symbol);
-
-        if (tradingSource?.symbol && currentNormalized !== backtestNormalized) {
-            console.log(`Chart mismatch: viewing "${this.ui.charts.trading_pair}" but backtest ran on "${tradingSource.symbol}". Skipping markers.`);
-            return;
-        }
-
-        // Call the chart function to show all trade markers
-        if (typeof this.ui.charts.setTradeMarkers === 'function') {
-            this.ui.charts.setTradeMarkers(trades);
-        } else {
-            console.warn('setTradeMarkers function not available on charts');
-        }
-    }
-
-    // Clear trade markers from chart
-    clearTradeMarkers() {
-        if (this.ui.charts && typeof this.ui.charts.clearTradeMarkers === 'function') {
-            this.ui.charts.clearTradeMarkers();
-        }
-    }
-
 
 }

src/static/charts.js

@@ -80,7 +80,7 @@ class Charts {
 	        crosshair: {
 		        mode: LightweightCharts.CrosshairMode.Normal,
 	        },
-	        rightPriceScale: {
+	        priceScale: {
 		        borderColor: 'rgba(197, 203, 206, 0.8)',
 	        },
 	        timeScale: {
@@ -89,22 +89,8 @@ class Charts {
 		        secondsVisible: false,
 		        barSpacing: 6
 	        },
-	        handleScroll: true,
+	        handleScroll: true
-	        localization: {
-	            // Display times in UTC to match server data
-	            timeFormatter: (timestamp) => {
-	                const date = new Date(timestamp * 1000);
-	                const day = date.getUTCDate().toString().padStart(2, '0');
-	                const months = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'];
-	                const month = months[date.getUTCMonth()];
-	                const year = date.getUTCFullYear().toString().slice(-2);
-	                const hours = date.getUTCHours().toString().padStart(2, '0');
-	                const minutes = date.getUTCMinutes().toString().padStart(2, '0');
-	                return `${day}/${month}/${year} ${hours}:${minutes}`;
-	            }
-	        }
         });
-
         return chart;
     }
 
@@ -233,116 +219,5 @@ class Charts {
         this.bound_charts[3].timeScale().subscribeVisibleTimeRangeChange(syncFromChart(3));
     }
 
-    // Set trade markers on chart for all trades in backtest results
-    setTradeMarkers(trades) {
-        if (!this.candleSeries) {
-            console.warn('Candlestick series not available');
-            return;
-        }
-
-        if (!trades || trades.length === 0) {
-            console.log('No trades to display as markers');
-            return;
-        }
-
-        const candleData = this.price_history || [];
-        if (candleData.length === 0) {
-            console.warn('No candle data available for markers');
-            return;
-        }
-
-        // Get the time range of loaded candle data
-        const minCandleTime = candleData[0].time;
-        const maxCandleTime = candleData[candleData.length - 1].time;
-        console.log(`Chart data range: ${minCandleTime} to ${maxCandleTime}`);
-
-        // Build markers array from all trades
-        const markers = [];
-
-        trades.forEach(trade => {
-            const openTime = this.dateStringToUnixTimestamp(trade.open_datetime);
-            const closeTime = trade.close_datetime ? this.dateStringToUnixTimestamp(trade.close_datetime) : null;
-
-            // Add entry marker if within chart data range
-            if (openTime && openTime >= minCandleTime && openTime <= maxCandleTime) {
-                const matchedOpenTime = this.findNearestCandleTime(openTime, candleData);
-                markers.push({
-                    time: matchedOpenTime,
-                    position: 'belowBar',
-                    color: '#26a69a',
-                    shape: 'arrowUp',
-                    text: 'BUY @ ' + (trade.open_price ? trade.open_price.toFixed(2) : '')
-                });
-            }
-
-            // Add exit marker if within chart data range
-            if (closeTime && closeTime >= minCandleTime && closeTime <= maxCandleTime) {
-                const matchedCloseTime = this.findNearestCandleTime(closeTime, candleData);
-                markers.push({
-                    time: matchedCloseTime,
-                    position: 'aboveBar',
-                    color: '#ef5350',
-                    shape: 'arrowDown',
-                    text: 'SELL @ ' + (trade.close_price ? trade.close_price.toFixed(2) : '')
-                });
-            }
-        });
-
-        if (markers.length === 0) {
-            console.log('No trades fall within the loaded chart data timespan');
-            return;
-        }
-
-        // Sort markers by time (required by lightweight-charts)
-        markers.sort((a, b) => a.time - b.time);
-
-        console.log(`Setting ${markers.length} trade markers on chart`);
-
-        // Set markers on the candlestick series
-        this.candleSeries.setMarkers(markers);
-    }
-
-    // Clear all trade markers from chart
-    clearTradeMarkers() {
-        if (this.candleSeries) {
-            this.candleSeries.setMarkers([]);
-            console.log('Trade markers cleared');
-        }
-    }
-
-    // Find the nearest candle time in the data
-    findNearestCandleTime(targetTime, candleData) {
-        if (!candleData || candleData.length === 0) {
-            return targetTime;
-        }
-
-        let nearestTime = candleData[0].time;
-        let minDiff = Math.abs(targetTime - nearestTime);
-
-        for (const candle of candleData) {
-            const diff = Math.abs(targetTime - candle.time);
-            if (diff < minDiff) {
-                minDiff = diff;
-                nearestTime = candle.time;
-            }
-            // Early exit if exact match
-            if (diff === 0) break;
-        }
-
-        return nearestTime;
-    }
-
-    // Convert datetime string to Unix timestamp in seconds
-    dateStringToUnixTimestamp(dateStr) {
-        if (!dateStr) return null;
-        try {
-            const date = new Date(dateStr);
-            return Math.floor(date.getTime() / 1000);
-        } catch (e) {
-            console.warn('Failed to parse date:', dateStr, e);
-            return null;
-        }
-    }
-
 
 }