gavindiaz/noteQuant-backtest
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refactor: clean up

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This commit is contained in:
moen0
2026-04-13 20:37:21 +02:00
parent 7d53f8589a
commit cd80a947a9
6 changed files with 27 additions and 92 deletions
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backend/api/routes.py
+2 -6
View File
@@ -614,7 +614,6 @@ def get_optimize(req: OptimizeRequest):
ranking_key = req.rank_objective
def _rank_results(items):
# Lower drawdown is better; all other objectives are maximize.
if ranking_key == "max_drawdown_pct":
return sorted(items, key=lambda x: x.get(ranking_key, 0))
return sorted(items, key=lambda x: x.get(ranking_key, 0), reverse=True)
@@ -666,10 +665,8 @@ def get_optimize(req: OptimizeRequest):
total_generated_combinations = base_count_without_sweep * sweep_factor
if sample_mode == "first":
# Stop iteration as soon as we hit the cap (no ghost work on remaining permutations).
combos = list(islice(_candidate_iter(), req.max_combinations))
else:
# Reservoir sampling keeps an unbiased random sample without storing all combos.
for generated_idx, candidate in enumerate(_candidate_iter(), start=1):
if len(combos) < req.max_combinations:
if len(combos) < req.max_combinations:
@@ -686,7 +683,6 @@ def get_optimize(req: OptimizeRequest):
started = time.perf_counter()
results = []
# Emit immediately so the UI can move off 0% as soon as the stream opens.
yield f"data: {json.dumps({'type': 'progress', 'progress': 0, 'processed': 0, 'total_combinations': executed_combinations, 'generated_combinations': total_generated_combinations, 'executed_combinations': executed_combinations, 'max_combinations': req.max_combinations, 'capped_by_max_combinations': capped_by_max_combinations, 'combo_sampling_mode': sample_mode, 'combo_sampling_seed': req.combo_sampling_seed, 'valid_results': 0, 'top_results': []})}\n\n"
for i, params in enumerate(combos):
@@ -924,12 +920,12 @@ def stream_backtest(
use_fvg: bool = True,
use_ob: bool = True,
proximity_pct: float = 0.5,
# FVG Quality
min_gap_size: float = 0.0,
impulse_multiplier: float = 0.0,
require_unmitigated_fvg: bool = True,
require_bos_confluence: bool = False,
# Order Block
min_ob_size: float = 0.0,
require_fvg_ob_confluence: bool = False,
# Liquidity
backend/engine/backtester.py
+6 -72
View File
@@ -30,54 +30,6 @@ def _apply_break_even_if_triggered(position, candle, strategy):
position["break_even_armed"] = True
def _apply_partial_tp_if_triggered(position, candle, strategy):
if not position:
return
if not getattr(strategy, "use_partial_tp", False):
return
if position.get("partial_taken"):
return
trigger_rr = float(getattr(strategy, "partial_tp_rr", 1.0) or 0.0)
if trigger_rr <= 0:
return
partial_pct = float(getattr(strategy, "partial_tp_percent", 0.0) or 0.0)
if partial_pct <= 0:
return
close_fraction = min(max(partial_pct / 100.0, 0.0), 1.0)
remaining_fraction = max(position.get("remaining_fraction", 1.0), 0.0)
if remaining_fraction <= 0:
position["partial_taken"] = True
return
close_fraction = min(close_fraction, remaining_fraction)
if close_fraction <= 0:
return
is_long = position["direction"] == "long"
entry = position["entry_price"]
risk_distance = max(position.get("risk_distance", 0.0), 0.0)
if risk_distance <= 0:
return
trigger_price = entry + (risk_distance * trigger_rr) if is_long else entry - (risk_distance * trigger_rr)
reached_trigger = candle.high >= trigger_price if is_long else candle.low <= trigger_price
if not reached_trigger:
return
lot_size = max(position.get("lot_size", 0.0), 0.0)
price_move = (trigger_price - entry) if is_long else (entry - trigger_price)
realized_piece = price_move * lot_size * close_fraction
position["realized_pnl"] = position.get("realized_pnl", 0.0) + realized_piece
position["remaining_fraction"] = max(0.0, remaining_fraction - close_fraction)
position["partial_taken"] = True
def run_backtest(candles, strategy, starting_balance, risk_reward=1.0,
max_daily_loss=0.0, max_consecutive_losses=0, risk_pct=1.0):
trades = []
@@ -89,7 +41,6 @@ def run_backtest(candles, strategy, starting_balance, risk_reward=1.0,
for i, candle in enumerate(candles):
if position:
_apply_partial_tp_if_triggered(position, candle, strategy)
_apply_break_even_if_triggered(position, candle, strategy)
is_long = position["direction"] == "long"
@@ -102,11 +53,9 @@ def run_backtest(candles, strategy, starting_balance, risk_reward=1.0,
exit_price = sl if hit_sl else tp
price_move = (exit_price - position["entry_price"]) if is_long else (position["entry_price"] - exit_price)
lot_size = max(position.get("lot_size", 0.0), 0.0)
remaining_fraction = max(position.get("remaining_fraction", 1.0), 0.0)
remaining_pnl = price_move * lot_size * remaining_fraction
pnl = position.get("realized_pnl", 0.0) + remaining_pnl
initial_risk = max(position.get("initial_risk_amount", 0.0), 1e-12)
r_multiple = pnl / initial_risk
pnl = price_move * lot_size
risk_distance = max(position.get("risk_distance", 0.0), 1e-12)
r_multiple = price_move / risk_distance
trades.append(Trade(
enter_time=position["enter_time"],
@@ -116,8 +65,6 @@ def run_backtest(candles, strategy, starting_balance, risk_reward=1.0,
exit_price=exit_price,
pnl=pnl,
r_multiple=r_multiple,
partial_tp_taken=bool(position.get("partial_taken", False)),
partial_tp_realized_pnl=float(position.get("realized_pnl", 0.0) or 0.0),
))
position = None
@@ -171,10 +118,6 @@ def run_backtest(candles, strategy, starting_balance, risk_reward=1.0,
"risk_distance": sl_distance,
"lot_size": lot_size,
"break_even_armed": False,
"partial_taken": False,
"remaining_fraction": 1.0,
"realized_pnl": 0.0,
"initial_risk_amount": risk_amount,
}
return trades
@@ -199,7 +142,6 @@ def run_backtest_stream(candles, strategy, starting_balance, risk_reward=1.0,
yield {"type": "progress", "processed_candles": i, "total_candles": total}
if position:
_apply_partial_tp_if_triggered(position, candle, strategy)
_apply_break_even_if_triggered(position, candle, strategy)
is_long = position["direction"] == "long"
@@ -212,11 +154,9 @@ def run_backtest_stream(candles, strategy, starting_balance, risk_reward=1.0,
exit_price = sl if hit_sl else tp
price_move = (exit_price - position["entry_price"]) if is_long else (position["entry_price"] - exit_price)
lot_size = max(position.get("lot_size", 0.0), 0.0)
remaining_fraction = max(position.get("remaining_fraction", 1.0), 0.0)
remaining_pnl = price_move * lot_size * remaining_fraction
pnl = position.get("realized_pnl", 0.0) + remaining_pnl
initial_risk = max(position.get("initial_risk_amount", 0.0), 1e-12)
r_multiple = pnl / initial_risk
pnl = price_move * lot_size
risk_distance = max(position.get("risk_distance", 0.0), 1e-12)
r_multiple = price_move / risk_distance
trade = Trade(
enter_time=position["enter_time"],
@@ -226,8 +166,6 @@ def run_backtest_stream(candles, strategy, starting_balance, risk_reward=1.0,
exit_price=exit_price,
pnl=pnl,
r_multiple=r_multiple,
partial_tp_taken=bool(position.get("partial_taken", False)),
partial_tp_realized_pnl=float(position.get("realized_pnl", 0.0) or 0.0),
)
position = None
@@ -283,10 +221,6 @@ def run_backtest_stream(candles, strategy, starting_balance, risk_reward=1.0,
"risk_distance": sl_distance,
"lot_size": lot_size,
"break_even_armed": False,
"partial_taken": False,
"remaining_fraction": 1.0,
"realized_pnl": 0.0,
"initial_risk_amount": risk_amount,
}
yield {"type": "done", "total_candles": total}
backend/indicators/fvg.py
-9
View File
@@ -1,12 +1,4 @@
def find_fvgs(candles, min_gap_size=0.0, impulse_multiplier=0.0):
"""
Find Fair Value Gaps in candle data.
Args:
candles: list of Candle objects
min_gap_size: minimum gap size in price units to filter noise (0 = no filter)
impulse_multiplier: minimum body-to-avg ratio for the middle candle (0 = no filter)
"""
fvgs = []
avg_body = 0
@@ -50,7 +42,6 @@ def find_fvgs(candles, min_gap_size=0.0, impulse_multiplier=0.0):
"bottom": c3.high,
"mitigated": False,
})
# Mark mitigated FVGs
for fvg in fvgs:
if fvg["mitigated"]: