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https://github.com/BrentNeale1/fx-quant.git
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Add economic calendar module to filter trades near high-impact events
Introduces a full Trading Economics API pipeline that fetches, stores, and queries economic events (NFP, CPI, rate decisions, etc.) so the backtester can block trade entries within a configurable buffer window of high-impact releases — reducing slippage and false signals. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Brent Neale
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
b1f3a919bf
commit
546d7311ec
+126
-4
@@ -18,6 +18,78 @@ from config_loader import load_config, get_project_root
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from data_engine import detect_engulfing, add_pivot_points
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# ---------------------------------------------------------------------------
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# Economic calendar helpers
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# ---------------------------------------------------------------------------
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def fetch_calendar_for_backtest(instrument, supabase_client, cfg):
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"""
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Load calendar events from Supabase filtered by the currencies in
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the instrument and the configured impact threshold.
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Returns a DataFrame or None if calendar is disabled/empty.
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"""
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from economic_calendar import fetch_calendar_from_supabase
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cal_cfg = cfg.get("economic_calendar", {})
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if not cal_cfg.get("enabled", False):
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return None
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table = cal_cfg.get("table", "economic_calendar")
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threshold = cal_cfg.get("impact_threshold", "High")
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impact_map = {"Low": 1, "Medium": 2, "High": 3}
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impact_min = impact_map.get(threshold, 3)
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# Derive currencies from instrument (e.g. EUR_USD -> [EUR, USD])
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currencies = instrument.split("_")
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print(f" Loading economic calendar events (currencies={currencies}, impact>={threshold})...")
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cal_df = fetch_calendar_from_supabase(
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supabase_client,
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table=table,
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currencies=currencies,
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impact_min=impact_min,
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)
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if cal_df.empty:
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print(" No calendar events found.")
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return None
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print(f" Loaded {len(cal_df)} calendar events.")
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return cal_df
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def is_near_event(bar_time, calendar_df, buffer_minutes=30):
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"""
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O(log n) check whether bar_time is within buffer_minutes of any
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calendar event, using DatetimeIndex slicing.
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"""
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if calendar_df is None or calendar_df.empty:
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return False
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# Ensure we have a DatetimeIndex for fast slicing
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if not isinstance(calendar_df.index, pd.DatetimeIndex):
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return False
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bar_ts = pd.Timestamp(bar_time, tz="UTC")
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window_start = bar_ts - pd.Timedelta(minutes=buffer_minutes)
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window_end = bar_ts + pd.Timedelta(minutes=buffer_minutes)
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nearby = calendar_df.loc[window_start:window_end]
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return len(nearby) > 0
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def _prepare_calendar_index(calendar_df):
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"""
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Set event_datetime as a sorted DatetimeIndex for O(log n) lookups.
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Returns the prepared DataFrame or None.
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"""
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if calendar_df is None or calendar_df.empty:
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return None
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cal = calendar_df.copy()
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cal["event_datetime"] = pd.to_datetime(cal["event_datetime"], utc=True)
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cal = cal.set_index("event_datetime").sort_index()
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return cal
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# ---------------------------------------------------------------------------
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# Data fetching
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# ---------------------------------------------------------------------------
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@@ -314,7 +386,8 @@ def _find_tp_levels_short(level_values, entry_lv_idx):
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# Dual take-profit backtest engine
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# ---------------------------------------------------------------------------
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def run_backtest_dual_tp(df, strategy_cfg):
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def run_backtest_dual_tp(df, strategy_cfg, calendar_df=None,
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event_buffer_minutes=30):
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"""
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Backtest engine supporting per-trade SL/TP with partial closes.
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@@ -324,6 +397,9 @@ def run_backtest_dual_tp(df, strategy_cfg):
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- On TP2 hit: close remaining 50%.
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- On SL hit: close full remaining position.
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If calendar_df is provided, entries within event_buffer_minutes of a
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high-impact event are blocked.
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Returns dict with equity_curve, trades, metrics (same interface as run_backtest).
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"""
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trade_size_pct = strategy_cfg.get("trade_size_pct_of_equity", 0.01)
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@@ -344,6 +420,10 @@ def run_backtest_dual_tp(df, strategy_cfg):
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position_size = 0.0
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half_closed = False
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# Calendar event filter
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cal_indexed = _prepare_calendar_index(calendar_df)
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blocked_by_calendar = 0
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equity_curve = []
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trades = []
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@@ -494,6 +574,11 @@ def run_backtest_dual_tp(df, strategy_cfg):
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if not in_position and not stopped:
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sig = signals[i]
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if sig in (1, -1) and not np.isnan(sl_col[i]):
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# Block entry if near a high-impact economic event
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if is_near_event(bar_time, cal_indexed, event_buffer_minutes):
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blocked_by_calendar += 1
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equity_curve.append(equity)
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continue
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direction = sig
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entry_price = bar_close
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sl_price = sl_col[i]
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@@ -546,6 +631,10 @@ def run_backtest_dual_tp(df, strategy_cfg):
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equity_series = pd.Series(equity_curve, index=df.index, name="equity")
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metrics = compute_metrics(equity_series, trades, starting_equity)
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if blocked_by_calendar > 0:
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print(f" Calendar filter blocked {blocked_by_calendar} trade entries.")
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metrics["blocked_by_calendar"] = blocked_by_calendar
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return {
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"equity_curve": equity_series,
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"trades": trades,
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@@ -557,9 +646,11 @@ def run_backtest_dual_tp(df, strategy_cfg):
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# Backtest engine (SMA cross — original)
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# ---------------------------------------------------------------------------
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def run_backtest(df, strategy_cfg):
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def run_backtest(df, strategy_cfg, calendar_df=None, event_buffer_minutes=30):
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"""
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Walk through bars, track position, equity, trades.
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If calendar_df is provided, entries within event_buffer_minutes of a
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high-impact event are blocked.
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Returns dict with equity_curve (Series), trades (list of dicts), metrics (dict).
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"""
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trade_size_pct = strategy_cfg.get("trade_size_pct_of_equity", 0.01)
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@@ -572,6 +663,10 @@ def run_backtest(df, strategy_cfg):
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position_size = 0.0
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stopped = False
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# Calendar event filter
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cal_indexed = _prepare_calendar_index(calendar_df)
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blocked_by_calendar = 0
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equity_curve = []
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trades = []
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@@ -622,6 +717,11 @@ def run_backtest(df, strategy_cfg):
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# Position change
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if sig != position:
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if sig == 1 and position == 0:
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# Block entry if near a high-impact economic event
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if is_near_event(bar_time, cal_indexed, event_buffer_minutes):
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blocked_by_calendar += 1
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equity_curve.append(equity)
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continue
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# Enter long
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position_size = equity * trade_size_pct
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trades.append({
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@@ -650,6 +750,10 @@ def run_backtest(df, strategy_cfg):
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equity_series = pd.Series(equity_curve, index=df.index, name="equity")
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metrics = compute_metrics(equity_series, trades, starting_equity)
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if blocked_by_calendar > 0:
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print(f" Calendar filter blocked {blocked_by_calendar} trade entries.")
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metrics["blocked_by_calendar"] = blocked_by_calendar
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return {
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"equity_curve": equity_series,
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"trades": trades,
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@@ -840,7 +944,17 @@ def main():
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print(f"Instruments: {instruments}")
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print(f"Granularities: {granularities}\n")
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# Economic calendar config
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cal_cfg = cfg.get("economic_calendar", {})
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cal_enabled = cal_cfg.get("enabled", False)
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event_buffer_minutes = cal_cfg.get("event_buffer_minutes", 30)
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for instrument in instruments:
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# Load calendar data once per instrument (shared across granularities)
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calendar_df = None
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if cal_enabled:
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calendar_df = fetch_calendar_for_backtest(instrument, sb, cfg)
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for granularity in granularities:
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print(f"=== {instrument} / {granularity} ===")
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@@ -860,9 +974,17 @@ def main():
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continue
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if strategy_cfg["rule"] == "pivot_retest_engulfing":
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results = run_backtest_dual_tp(df, strategy_cfg)
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results = run_backtest_dual_tp(
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df, strategy_cfg,
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calendar_df=calendar_df,
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event_buffer_minutes=event_buffer_minutes,
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)
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else:
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results = run_backtest(df, strategy_cfg)
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results = run_backtest(
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df, strategy_cfg,
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calendar_df=calendar_df,
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event_buffer_minutes=event_buffer_minutes,
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)
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save_results(instrument, granularity, results, results["metrics"])
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print("Backtesting complete.")
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