Add backtest debugging features: timestamps, chart markers, and alerts
- Add Open/Close Time columns to Trades Executed table - Display trade markers (buy/sell arrows) automatically on chart - Clear markers when closing results dialog or running new test - Collect notify_user alerts with timestamps during backtesting - Display Strategy Alerts section in backtest results - Fix timestamp conversion: use unit='s' for EDM timestamps (not 'ms') - Fix trade datetime extraction using data feed's datetime method Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
parent
3e6463e4b3
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307f251576
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@ -49,6 +49,9 @@ class BacktestStrategyInstance(StrategyInstance):
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# Initialize last_valid_values for indicators
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self.last_valid_values={}
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# Initialize collected alerts for backtest results
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self.collected_alerts = []
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def set_backtrader_strategy(self, backtrader_strategy: bt.Strategy):
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"""
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Sets the backtrader_strategy and initializes broker-dependent attributes.
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@ -147,14 +150,15 @@ class BacktestStrategyInstance(StrategyInstance):
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If no last valid value exists, searches forward for the next valid value.
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If no valid value is found, returns a default value (e.g., 1).
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"""
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logger.debug(f"Backtester is retrieving indicator '{indicator_name}' from precomputed data.")
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logger.info(f"[BACKTEST] process_indicator called: indicator='{indicator_name}', output='{output_field}'")
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if self.backtrader_strategy is None:
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logger.error("Backtrader strategy is not set in StrategyInstance.")
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return None
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df = self.backtrader_strategy.precomputed_indicators.get(indicator_name)
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if df is None:
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logger.error(f"Indicator '{indicator_name}' not found.")
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logger.error(f"[BACKTEST DEBUG] Indicator '{indicator_name}' not found in precomputed_indicators!")
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logger.error(f"[BACKTEST DEBUG] Available indicators: {list(self.backtrader_strategy.precomputed_indicators.keys())}")
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return None
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idx = self.backtrader_strategy.indicator_pointers.get(indicator_name, 0)
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@ -165,6 +169,10 @@ class BacktestStrategyInstance(StrategyInstance):
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# Retrieve the value at the current index
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value = df.iloc[idx].get(output_field)
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# Log indicator values for debugging (first 10 and every 50th)
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if idx < 10 or idx % 50 == 0:
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logger.info(f"[BACKTEST] process_indicator('{indicator_name}', '{output_field}') at idx={idx}: raw_value={value}")
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if pd.isna(value):
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# Check if we have a cached last valid value
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last_valid_value = self.last_valid_values.get(indicator_name, {}).get(output_field)
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@ -195,6 +203,10 @@ class BacktestStrategyInstance(StrategyInstance):
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if indicator_name not in self.last_valid_values:
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self.last_valid_values[indicator_name] = {}
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self.last_valid_values[indicator_name][output_field] = value
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# Log the returned value for debugging
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idx = self.backtrader_strategy.indicator_pointers.get(indicator_name, 0)
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if idx < 10 or idx % 50 == 0:
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logger.info(f"[BACKTEST] process_indicator returning: {indicator_name}.{output_field} = {value}")
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return value
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# 3. Override get_current_price
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@ -303,10 +315,35 @@ class BacktestStrategyInstance(StrategyInstance):
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# 9. Override notify_user
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def notify_user(self, message: str):
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"""
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Suppresses user notifications and instead logs them.
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Collects notifications with timestamps for display in backtest results.
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:param message: Notification message.
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"""
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logger.debug(f"Backtest notification: {message}")
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timestamp = self.get_current_candle_datetime()
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alert = {
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'timestamp': timestamp.isoformat() if timestamp else None,
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'message': message
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}
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self.collected_alerts.append(alert)
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logger.debug(f"Backtest notification: {message} (at {timestamp})")
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def get_current_candle_datetime(self) -> dt.datetime:
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"""
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Gets the datetime of the current candle from backtrader's data feed.
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"""
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if self.backtrader_strategy is None:
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return dt.datetime.now()
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try:
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# Use the data feed's datetime method to get proper datetime
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return self.backtrader_strategy.data.datetime.datetime(0)
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except Exception as e:
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logger.warning(f"Could not get candle datetime: {e}")
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return dt.datetime.now()
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def get_collected_alerts(self) -> list:
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"""
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Returns the list of collected alerts for inclusion in backtest results.
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"""
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return self.collected_alerts
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def save_context(self):
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"""
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@ -323,11 +323,23 @@ class Backtester:
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def precompute_indicators(self, indicators_definitions: list, user_name: str, data_feed: pd.DataFrame) -> dict:
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"""
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Precompute indicator values and return a dictionary of DataFrames.
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Precompute indicator values directly on the backtest data feed.
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IMPORTANT: This computes indicators on the actual backtest candle data,
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ensuring the indicator values align with the price data used in the backtest.
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Previously, this fetched fresh/latest candles which caused misalignment.
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"""
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import json as json_module # Local import to avoid conflicts
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precomputed_indicators = {}
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total_candles = len(data_feed)
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logger.info(f"[BACKTEST] precompute_indicators called with {len(indicators_definitions)} indicator definitions")
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logger.info(f"[BACKTEST] user_name: {user_name}, total_candles: {total_candles}")
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logger.info(f"[BACKTEST] data_feed columns: {list(data_feed.columns)}")
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logger.info(f"[BACKTEST] data_feed first row: {data_feed.iloc[0].to_dict() if len(data_feed) > 0 else 'empty'}")
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logger.info(f"[BACKTEST] data_feed last row: {data_feed.iloc[-1].to_dict() if len(data_feed) > 0 else 'empty'}")
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# Aggregate requested outputs for each indicator
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indicator_outputs = {}
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for indicator_def in indicators_definitions:
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@ -350,44 +362,83 @@ class Backtester:
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# If output is None, we need all outputs
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indicator_outputs[indicator_name] = None # None indicates all outputs
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# Now, precompute each unique indicator with the required outputs
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for indicator_name, outputs in indicator_outputs.items():
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# Compute the indicator values
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indicator_data = self.indicators_manager.get_latest_indicator_data(
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user_name=user_name,
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indicator_name=indicator_name,
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num_results=total_candles
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# Get user ID for indicator lookup
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user_id = self.data_cache.get_datacache_item(
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item_name='id',
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cache_name='users',
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filter_vals=('user_name', user_name)
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)
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if not indicator_data:
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logger.warning(f"No data returned for indicator '{indicator_name}'. Skipping.")
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logger.info(f"[BACKTEST] indicator_outputs to precompute: {indicator_outputs}")
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# Prepare candle data for indicator calculation
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# Convert data_feed to the format expected by indicators
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candle_data = data_feed.copy()
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# Ensure required columns exist
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if 'time' not in candle_data.columns and candle_data.index.name == 'datetime':
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candle_data['time'] = candle_data.index.astype(np.int64) // 10**6 # Convert to milliseconds
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for indicator_name, outputs in indicator_outputs.items():
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logger.info(f"[BACKTEST] Computing indicator '{indicator_name}' on backtest data feed")
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try:
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# Fetch indicator definition from cache
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indicators = self.data_cache.get_rows_from_datacache(
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cache_name='indicators',
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filter_vals=[('creator', str(user_id)), ('name', indicator_name)]
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)
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if indicators.empty:
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logger.warning(f"[BACKTEST] Indicator '{indicator_name}' not found for user '{user_name}' (id={user_id}). Skipping.")
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continue
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data = indicator_data.get(indicator_name)
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indicator = indicators.iloc[0]
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kind = indicator['kind']
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properties = json_module.loads(indicator['properties']) if isinstance(indicator['properties'], str) else indicator['properties']
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# Convert the data to a DataFrame
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if isinstance(data, list):
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df = pd.DataFrame(data)
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elif isinstance(data, dict):
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df = pd.DataFrame([data])
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else:
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logger.warning(f"Unexpected data format for indicator '{indicator_name}'. Skipping.")
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logger.info(f"[BACKTEST] Indicator '{indicator_name}' is of kind '{kind}' with properties: {properties}")
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# Get the indicator class from the registry
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indicator_class = self.indicators_manager.indicator_registry.get(kind)
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if not indicator_class:
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logger.warning(f"[BACKTEST] Unknown indicator kind '{kind}' for '{indicator_name}'. Skipping.")
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continue
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# Instantiate and calculate
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indicator_obj = indicator_class(name=indicator_name, indicator_type=kind, properties=properties)
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result_df = indicator_obj.calculate(candles=candle_data, user_name=user_name, num_results=total_candles)
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if result_df is None or (isinstance(result_df, pd.DataFrame) and result_df.empty):
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logger.warning(f"[BACKTEST] No data computed for indicator '{indicator_name}'. Skipping.")
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continue
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logger.info(f"[BACKTEST] Computed indicator '{indicator_name}': {len(result_df)} rows, columns: {list(result_df.columns)}")
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# Log first few values for debugging
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if len(result_df) > 0:
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logger.info(f"[BACKTEST] First 3 rows of '{indicator_name}': {result_df.head(3).to_dict('records')}")
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logger.info(f"[BACKTEST] Last 3 rows of '{indicator_name}': {result_df.tail(3).to_dict('records')}")
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# If outputs is None, keep all outputs
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if outputs is not None:
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# Include 'time' and requested outputs
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columns_to_keep = ['time'] + list(outputs)
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missing_columns = [col for col in columns_to_keep if col not in df.columns]
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missing_columns = [col for col in columns_to_keep if col not in result_df.columns]
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if missing_columns:
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logger.warning(f"Indicator '{indicator_name}' missing columns: {missing_columns}. Skipping.")
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continue
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df = df[columns_to_keep]
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logger.warning(f"[BACKTEST] Indicator '{indicator_name}' missing columns: {missing_columns}. Available: {list(result_df.columns)}")
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# Try to continue with available columns
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columns_to_keep = [c for c in columns_to_keep if c in result_df.columns]
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result_df = result_df[columns_to_keep]
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# Reset index and store the DataFrame
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df.reset_index(drop=True, inplace=True)
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precomputed_indicators[indicator_name] = df
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logger.debug(f"Precomputed indicator '{indicator_name}' with {len(df)} data points.")
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result_df.reset_index(drop=True, inplace=True)
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precomputed_indicators[indicator_name] = result_df
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logger.info(f"[BACKTEST] Precomputed indicator '{indicator_name}' with {len(result_df)} data points.")
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except Exception as e:
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logger.error(f"[BACKTEST] Error computing indicator '{indicator_name}': {e}", exc_info=True)
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continue
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return precomputed_indicators
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@ -428,6 +479,18 @@ class Backtester:
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indicators_definitions = strategy_components.get('indicators', [])
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precomputed_indicators = self.precompute_indicators(indicators_definitions, user_name, data_feed)
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# Align data feed with indicator data by trimming to match the shortest indicator
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# Indicators skip their warmup period, so they have fewer rows than the raw candle data
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if precomputed_indicators:
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min_indicator_len = min(len(df) for df in precomputed_indicators.values())
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original_len = len(data_feed)
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if min_indicator_len < original_len:
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# Trim the data feed from the beginning to align with indicators
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warmup_period = original_len - min_indicator_len
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data_feed = data_feed.iloc[warmup_period:].reset_index(drop=True)
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logger.info(f"[BACKTEST] Trimmed data feed from {original_len} to {len(data_feed)} rows to align with indicators (warmup period: {warmup_period})")
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logger.info("Backtest data prepared successfully.")
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return data_feed, precomputed_indicators
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@ -446,7 +509,16 @@ class Backtester:
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try:
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# **Convert 'time' to 'datetime' if necessary**
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if 'time' in data_feed.columns:
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data_feed['datetime'] = pd.to_datetime(data_feed['time'], unit='ms')
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# Time values from EDM are Unix timestamps in SECONDS, not milliseconds
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data_feed['datetime'] = pd.to_datetime(data_feed['time'], unit='s')
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# DEBUG: Log first and last timestamps to verify conversion
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if len(data_feed) > 0:
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first_time = data_feed['time'].iloc[0]
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last_time = data_feed['time'].iloc[-1]
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first_dt = data_feed['datetime'].iloc[0]
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last_dt = data_feed['datetime'].iloc[-1]
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logger.info(f"[DEBUG DATETIME FIX] First raw time: {first_time}, converted: {first_dt}")
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logger.info(f"[DEBUG DATETIME FIX] Last raw time: {last_time}, converted: {last_dt}")
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data_feed.set_index('datetime', inplace=True)
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logger.info("Converted 'time' to 'datetime' and set as index in data_feed.")
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@ -509,6 +581,12 @@ class Backtester:
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'progress': 100}}
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, room=socket_conn_id)
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# Get collected alerts from strategy instance
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collected_alerts = strategy_instance.get_collected_alerts()
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# Get trading source info for chart validation
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trading_source = msg_data.get('trading_source', {})
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# Prepare the results to pass into the callback
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backtest_results = {
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"success": True, # Indicate success
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@ -517,6 +595,8 @@ class Backtester:
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"run_duration": run_duration,
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"equity_curve": equity_curve,
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"trades": trades,
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"alerts": collected_alerts,
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"trading_source": trading_source,
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}
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logger.info("Backtest executed successfully.")
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@ -597,6 +677,8 @@ class Backtester:
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}
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logger.info(f"Using default_source for backtest data: {source}")
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strategy_components['data_sources'] = [source]
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# Store trading source in msg_data for inclusion in backtest results
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msg_data['trading_source'] = source
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try:
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data_feed, precomputed_indicators = self.prepare_backtest_data(msg_data, strategy_components)
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@ -87,9 +87,13 @@ class MappedStrategy(bt.Strategy):
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f"notify_trade called for trade {trade.ref}, PnL: {trade.pnl}, Status: {trade.status_names[trade.status]}")
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if trade.isopen:
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# Trade just opened
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# Trade just opened - use current bar's datetime from data feed
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current_dt = self.data.datetime.datetime(0)
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open_datetime = current_dt.isoformat() if current_dt else None
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# Debug logging
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raw_dt = self.data.datetime[0]
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logger.info(f"[DEBUG] Trade open - raw datetime[0]={raw_dt}, converted={current_dt}, iso={open_datetime}")
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self.log(f"TRADE OPENED, Size: {trade.size}, Price: {trade.price}")
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open_datetime = bt.num2date(trade.dtopen).isoformat() if trade.dtopen else None
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trade_info = {
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'ref': trade.ref,
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'size': trade.size,
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@ -99,9 +103,10 @@ class MappedStrategy(bt.Strategy):
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# Store the trade_info with trade.ref as key
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self.open_trades[trade.ref] = trade_info
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elif trade.isclosed:
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# Trade just closed
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# Trade just closed - use current bar's datetime from data feed
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current_dt = self.data.datetime.datetime(0)
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close_datetime = current_dt.isoformat() if current_dt else None
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self.log(f"TRADE CLOSED, GROSS P/L: {trade.pnl}, NET P/L: {trade.pnlcomm}")
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close_datetime = bt.num2date(trade.dtclose).isoformat() if trade.dtclose else None
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# Retrieve open trade details
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trade_info = self.open_trades.pop(trade.ref, {})
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# Get the close price from data feed
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@ -129,9 +134,28 @@ class MappedStrategy(bt.Strategy):
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self.current_step += 1
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# Debug: Log current price and indicator values every N steps
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if self.current_step <= 10 or self.current_step % 50 == 0:
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current_price = self.data.close[0]
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logger.info(f"[BACKTEST STEP {self.current_step}] Price: {current_price:.2f}")
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# Log all indicator values at this step
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for ind_name, df in self.precomputed_indicators.items():
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idx = self.indicator_pointers.get(ind_name, 0)
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if idx < len(df):
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row = df.iloc[idx]
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# Log all columns except 'time'
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values = {col: row[col] for col in df.columns if col != 'time'}
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logger.info(f"[BACKTEST STEP {self.current_step}] Indicator '{ind_name}' at idx {idx}: {values}")
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# Execute the strategy logic
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self.execute_strategy()
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# Advance indicator pointers for the next candle
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for name in self.indicator_names:
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if name in self.indicator_pointers:
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self.indicator_pointers[name] += 1
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# Check if we're at the second-to-last bar
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if self.current_step == (self.p.data_length - 1):
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if self.position:
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@ -207,6 +207,9 @@ class Backtesting {
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this.setText(this.progressBar, '0%');
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this.resultsDisplay.innerHTML = ''; // Clear previous results
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this.displayMessage('Backtest started...', 'blue');
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// Clear previous trade markers from chart
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this.clearTradeMarkers();
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}
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displayTestResults(results) {
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@ -253,6 +256,8 @@ class Backtesting {
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<thead>
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<tr>
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<th>Trade ID</th>
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<th>Open Time</th>
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<th>Close Time</th>
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<th>Size</th>
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<th>Open Price</th>
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<th>Close Price</th>
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@ -266,10 +271,14 @@ class Backtesting {
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const openPrice = trade.open_price != null ? trade.open_price.toFixed(2) : 'N/A';
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const closePrice = trade.close_price != null ? trade.close_price.toFixed(2) : 'N/A';
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const pnl = trade.pnl != null ? trade.pnl.toFixed(2) : 'N/A';
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const openTime = trade.open_datetime ? this.formatTradeDateTime(trade.open_datetime) : 'N/A';
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const closeTime = trade.close_datetime ? this.formatTradeDateTime(trade.close_datetime) : 'N/A';
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html += `
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<tr>
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<td>${trade.ref}</td>
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<td>${openTime}</td>
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<td>${closeTime}</td>
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<td>${size}</td>
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<td>${openPrice}</td>
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<td>${closePrice}</td>
|
||||
|
|
@ -277,6 +286,9 @@ class Backtesting {
|
|||
</tr>
|
||||
`;
|
||||
});
|
||||
|
||||
// Automatically show trade markers on chart
|
||||
this.showTradeMarkersOnChart(results.trades, results.trading_source);
|
||||
html += `
|
||||
</tbody>
|
||||
</table>
|
||||
|
|
@ -287,6 +299,36 @@ 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);
|
||||
}
|
||||
|
|
@ -544,6 +586,9 @@ class Backtesting {
|
|||
this.hideElement(this.resultsContainer);
|
||||
this.hideElement(this.progressContainer);
|
||||
this.clearMessage();
|
||||
|
||||
// Clear trade markers from chart
|
||||
this.clearTradeMarkers();
|
||||
}
|
||||
|
||||
clearForm() {
|
||||
|
|
@ -641,5 +686,54 @@ class Backtesting {
|
|||
return `${year}-${month}-${day}T${hours}:${minutes}`;
|
||||
}
|
||||
|
||||
// Format trade datetime for display in trades table
|
||||
formatTradeDateTime(dateTimeStr) {
|
||||
if (!dateTimeStr) return 'N/A';
|
||||
try {
|
||||
const date = new Date(dateTimeStr);
|
||||
const pad = (num) => num.toString().padStart(2, '0');
|
||||
const month = pad(date.getMonth() + 1);
|
||||
const day = pad(date.getDate());
|
||||
const hours = pad(date.getHours());
|
||||
const minutes = pad(date.getMinutes());
|
||||
return `${month}/${day} ${hours}:${minutes}`;
|
||||
} 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();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -219,5 +219,116 @@ 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;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue