mirror of
https://github.com/KhizarImran/backtestingfx.git
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Khizar Imran
44c36b2563
Process opening SL/TP triggers before intrabar range touches, preserving midpoint triggers and exit costs. Add regression coverage and document execution and migration behavior. Refs #8 Codex 🤖, On behalf of Khizar
110 lines
5.5 KiB
Python
110 lines
5.5 KiB
Python
import unittest
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import pandas as pd
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from backtestingfx import Backtest, Strategy
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class IntrabarExecutionTest(unittest.TestCase):
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def test_gap_fills_and_intrabar_precedence_through_python_strategy(self):
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# Offsets describe a long; reflect prices for a short. Include gaps whose
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# ranges never reach the old level, and gaps that reverse through both levels.
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cases = [
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("stop gap", -0.02, -0.02, -0.02, -0.02, -0.02),
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("stop gap recovers", -0.02, -0.02, 0.02, 0.0, -0.02),
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("target gap", 0.02, 0.02, 0.02, 0.02, 0.02),
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("target gap reverses", 0.02, -0.02, 0.02, 0.0, 0.02),
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("stop at open", -0.01, -0.02, 0.02, 0.0, -0.01),
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("target at open", 0.01, -0.02, 0.02, 0.0, 0.01),
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("intrabar stop", 0.0, -0.015, 0.005, 0.002, -0.01),
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("intrabar target", 0.0, -0.005, 0.015, 0.002, 0.01),
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("ambiguous range", 0.0, -0.02, 0.02, 0.0, -0.01),
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("no trigger", 0.0, -0.005, 0.005, 0.002, 0.002),
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]
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index = pd.date_range("2026-01-01", periods=2, freq="h", tz="UTC")
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for is_long in (True, False):
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direction = 1 if is_long else -1
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for name, opening, low, high, close, expected_exit in cases:
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with self.subTest(is_long=is_long, case=name):
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seen = []
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stop = 1.1 - direction * 0.01
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target = 1.1 + direction * 0.01
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class EnterOnce(Strategy):
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def next(self):
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seen.append(len(self.positions))
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if self.index == 0:
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order = self.buy if is_long else self.sell
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order(0.4, stop_loss=stop, take_profit=target)
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extremes = [1.1 + direction * low, 1.1 + direction * high]
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data = pd.DataFrame(
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{
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"open": [1.1, 1.1 + direction * opening],
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"high": [1.1, max(extremes)],
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"low": [1.1, min(extremes)],
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"close": [1.1, 1.1 + direction * close],
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},
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index=index,
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)
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stats = Backtest(
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data, EnterOnce, commission=7.0, spread=0.0001,
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quote_to_account=1.27,
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).run()
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self.assertEqual(stats.num_trades, 1)
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trade = stats.trades[0]
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self.assertEqual(trade.is_long, is_long)
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self.assertAlmostEqual(trade.lot_size, 0.4)
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self.assertAlmostEqual(trade.entry_price, 1.1 + direction * 0.00005)
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self.assertAlmostEqual(
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trade.exit_price, 1.1 + direction * (expected_exit - 0.00005)
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)
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pnl = (expected_exit - 0.0001) * 100_000 * 0.4 * 1.27 - 5.6
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self.assertAlmostEqual(trade.pnl, pnl)
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self.assertAlmostEqual(stats.final_cash, 10_000 + pnl)
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self.assertAlmostEqual(stats.equity_curve[-1], stats.final_cash)
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self.assertEqual(trade.entry_timestamp, int(index[0].timestamp()))
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self.assertEqual(trade.exit_timestamp, int(index[1].timestamp()))
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self.assertEqual(seen, [0, 1 if name == "no trigger" else 0])
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def test_new_entries_are_checked_from_the_following_bar(self):
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for is_long in (True, False):
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for count in (1, 2):
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with self.subTest(is_long=is_long, count=count):
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seen = []
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class EnterOnce(Strategy):
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def next(self):
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seen.append(len(self.positions))
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if self.index == 0:
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if is_long:
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self.buy(1.0, stop_loss=1.09, take_profit=1.11)
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else:
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self.sell(1.0, stop_loss=1.11, take_profit=1.09)
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index = pd.date_range("2026-01-01", periods=count, freq="h", tz="UTC")
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data = pd.DataFrame(
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{"open": [1.1] * count, "high": [1.12] * count,
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"low": [1.08] * count, "close": [1.1] * count},
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index=index,
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)
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stats = Backtest(data, EnterOnce, commission=0, spread=0).run()
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self.assertEqual(stats.num_trades, 1)
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trade = stats.trades[0]
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self.assertEqual(trade.entry_timestamp, int(index[0].timestamp()))
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self.assertEqual(trade.exit_timestamp, int(index[-1].timestamp()))
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if count == 1:
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# Final liquidation at close: the entry bar's range is not replayed.
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self.assertAlmostEqual(trade.exit_price, 1.1)
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self.assertAlmostEqual(trade.pnl, 0.0)
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self.assertEqual(seen, [0])
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else:
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# Both levels touched on the next bar: stop is processed before next().
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self.assertAlmostEqual(trade.exit_price, 1.09 if is_long else 1.11)
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self.assertAlmostEqual(trade.pnl, -1000.0)
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self.assertEqual(seen, [0, 0])
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if __name__ == "__main__":
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unittest.main()
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