Normalize FRAMA window ranges while preserving close, seed, historical window and zero-lag preprocessing. Add native custom_exit consumer contracts and fix the remaining index commentary. Refresh documentation, measured coverage and snapshot line positions without changing accepted diagnostics.
python -m unittest discover -s quickadapter/tests -v
```
+QuickAdapter regressions cover normalized FRAMA with an independent numerical
+oracle and `custom_exit` with native Freqtrade trades and filled orders. Callback
+cases include model expiration, outliers, reversal confirmation and persisted
+final take-profit trails for long and short positions, including restart behavior.
+
Both commands must be run from the repository root, which is what the
container's `--workdir /workspace` provides. To select one concern, pass a
pattern that matches the whole `module.Class.method` name; a bare substring
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"filesAnalyzed": 5,
show_missing = True
precision = 1
include_namespace_packages = True
-# measured 2026-10-02 at 71.6% — raise only, never lower
+# measured 2026-10-02 at 73.1% — raise only, never lower
fail_under = 68
-"""Position sizing and throttle contracts; requires the Freqtrade QA image."""
+"""Trade callbacks, position sizing and throttle contracts; requires the Freqtrade QA image."""
+import copy
import datetime
import math
import unittest
import numpy as np
import pandas as pd
from freqtrade.enums import RunMode
-from freqtrade.persistence import Trade
+from freqtrade.persistence import Order, Trade
from qa_support import PAIR, QaTestCase, model_config, temporary_directory
from QuickAdapterV3 import QuickAdapterV3
+from Utils import EXTREMA_COLUMN
def strategy(*, duration=10, natr=2.0, fraction=0.5, candle_secs=300):
config = model_config(
directory,
runmode=RunMode.BACKTEST,
- exit_pricing={"trade_natr_method": "quantile_interpolation"},
+ exit_pricing={
+ "trade_natr_method": "quantile_interpolation",
+ "final_take_profit_retracement_fraction": 0.25,
+ },
)
self.model = QuickAdapterV3(config)
self.model.freqai_info = config["freqai"]
"close": [100.0] * 3,
"natr_label_period_candles": [1.0, 4.0, 8.0],
"label_natr_multiplier": [3.0] * 3,
+ "do_predict": [1] * 3,
+ "DI_catch": [1] * 3,
+ EXTREMA_COLUMN: [0.0] * 3,
+ "minima_threshold": [-0.5] * 3,
+ "maxima_threshold": [0.5] * 3,
}
)
self.model.dp = SimpleNamespace(
),
)
- def position(self, short=False):
- return Trade(
+ def position(self, short=False, exit_stage=0):
+ position = Trade(
pair=PAIR,
open_rate=100.0,
open_date=self.now - datetime.timedelta(minutes=5),
fee_close=0.0,
orders=[],
)
+ position.orders = [
+ Order(
+ ft_order_side=position.exit_side,
+ ft_is_open=False,
+ status="closed",
+ filled=0.1,
+ ft_order_tag=f"take_profit_{position.trade_direction}_{stage}",
+ )
+ for stage in range(exit_stage)
+ ]
+ return position
+
+ def custom_data(self, position, store=None):
+ # Isolate database persistence only; all trade/exit calculations remain native.
+ store = {} if store is None else store
+ self.enterContext(
+ mock.patch.object(
+ position,
+ "get_custom_data",
+ side_effect=lambda key, default=None: store.get(key, default),
+ )
+ )
+ self.enterContext(
+ mock.patch.object(
+ position,
+ "set_custom_data",
+ side_effect=lambda key, value: store.update({key: value}),
+ )
+ )
+ return store
+
+ def exit(self, position, rate=100.0):
+ return self.model.custom_exit(PAIR, position, self.now, rate, 0.0)
def test_stoploss_distance_uses_current_not_entry_price(self):
# Entry NATR 4 has mean rank 0.25 in [4, 8], giving interpolated NATR 7.
direction = "short" if short else "long"
self.assertEqual(after, (stake_reduction, f"take_profit_{direction}_0"))
+ def test_expired_models_exit_before_order_stage_and_date_guards(self):
+ self.frame.loc[self.frame.index[-1], "date"] = pd.NaT
+ self.frame["do_predict"] = 2
+ self.frame["DI_catch"] = 0
+ for short in (False, True):
+ with self.subTest(short=short):
+ position = self.position(short)
+ position.orders.append(
+ Order(
+ ft_order_side=position.exit_side, ft_is_open=True, status="open", filled=0.0
+ )
+ )
+ store = self.custom_data(position, {"n_outliers": 17})
+ self.assertEqual(self.exit(position), "model_expired")
+ self.assertEqual(store, {"n_outliers": 17})
+
+ def test_outliers_count_once_per_valid_candle(self):
+ position = self.position()
+ store = self.custom_data(position, {"last_outlier_date": "not-a-date"})
+ self.frame["DI_catch"] = 0
+ self.assertIsNone(self.exit(position))
+ self.assertIsNone(self.exit(position))
+ self.assertEqual(store["n_outliers"], 1)
+ self.assertEqual(store["last_outlier_date"], self.now.isoformat())
+ self.now += datetime.timedelta(minutes=5)
+ self.frame.loc[self.frame.index[-1], "date"] = self.now
+ self.assertIsNone(self.exit(position))
+ self.assertEqual(store["n_outliers"], 2)
+ self.assertEqual(store["last_outlier_date"], self.now.isoformat())
+ self.frame.loc[self.frame.index[-1], "date"] = pd.NaT
+ self.assertIsNone(self.exit(position))
+ self.assertEqual(store["n_outliers"], 2)
+
+ def test_reversal_exits_require_strict_scores_and_real_price_confirmation(self):
+ self.model.reversal_confirmation.update(
+ {
+ "lookback_period_candles": 0,
+ "decay_fraction": 1.0,
+ "min_natr_multiplier_fraction": 0.5,
+ "max_natr_multiplier_fraction": 0.5,
+ }
+ )
+ for short, boundary, score, rate, tag in (
+ (False, 0.5, 0.6, 80.0, "maxima_detected_long"),
+ (True, -0.5, -0.6, 120.0, "minima_detected_short"),
+ ):
+ with self.subTest(short=short):
+ position = self.position(short)
+ self.custom_data(position)
+ self.frame[EXTREMA_COLUMN] = boundary
+ self.assertIsNone(self.exit(position, rate))
+ self.frame[EXTREMA_COLUMN] = score
+ self.assertIsNone(self.exit(position, 100.0))
+ for prediction, inlier in ((0, 1), (1, 0)):
+ self.frame["do_predict"] = prediction
+ self.frame["DI_catch"] = inlier
+ self.assertIsNone(self.exit(position, rate))
+ self.frame["do_predict"] = 1
+ self.frame["DI_catch"] = 1
+ self.assertEqual(self.exit(position, rate), tag)
+
+ def test_final_exit_guards_preserve_unarmed_state(self):
+ original = self.frame.copy()
+ for guard in ("empty", "open_order", "partial_stage", "invalid_date"):
+ with self.subTest(guard=guard):
+ self.frame = original.copy()
+ position = self.position(exit_stage=0 if guard == "partial_stage" else 3)
+ store = self.custom_data(position)
+ if guard == "empty":
+ self.frame = self.frame.iloc[:0]
+ elif guard == "open_order":
+ position.orders.append(
+ Order(
+ ft_order_side=position.exit_side,
+ ft_is_open=True,
+ status="open",
+ filled=0.0,
+ )
+ )
+ elif guard == "invalid_date":
+ self.frame.loc[self.frame.index[-1], "date"] = pd.NaT
+ self.assertIsNone(self.exit(position, 150.0))
+ self.assertEqual(store, {})
+
+ def test_final_exit_trails_survive_restart_and_confirm_on_a_later_candle(self):
+ key = QuickAdapterV3._FINAL_TAKE_PROFIT_STATE_KEY
+ for short, target, arm, best, retrace, tag in (
+ (False, 121.0, 130.0, 135.0, 110.0, "take_profit_long_final"),
+ (True, 79.0, 70.0, 65.0, 90.0, "take_profit_short_final"),
+ ):
+ with self.subTest(short=short):
+ self.now = datetime.datetime(2026, 1, 1, 0, 10, tzinfo=datetime.UTC)
+ self.frame.loc[self.frame.index[-1], "date"] = self.now
+ position = self.position(short, exit_stage=3)
+ store = self.custom_data(position)
+ # Entry NATR rank gives 7%; final fraction 1 and one-candle factor 1.
+ np.testing.assert_allclose(
+ self.model.get_take_profit_target(self.frame, position, 3),
+ (target, 21.0),
+ rtol=1e-12,
+ atol=0.0,
+ )
+ self.assertIsNone(self.exit(position, 100.0))
+ self.assertNotIn(key, store)
+ self.assertEqual(store["history"]["take_profit_price"], [(3, target)])
+ self.assertIsNone(self.exit(position, arm))
+ self.assertEqual(store[key]["best_rate"], arm)
+ self.assertAlmostEqual(store[key]["retracement_distance"], 5.25, places=12)
+ self.now += datetime.timedelta(minutes=5)
+ self.frame.loc[self.frame.index[-1], "date"] = self.now
+ self.assertIsNone(self.exit(position, best))
+ self.assertEqual(store[key]["best_rate"], best)
+ provider = self.model.dp
+ config = copy.deepcopy(self.model.config)
+ self.model = QuickAdapterV3(config)
+ self.model.freqai_info = config["freqai"]
+ self.model.bot_start()
+ self.model.dp = provider
+ position = self.position(short, exit_stage=3)
+ self.custom_data(position, store)
+ self.assertIsNone(self.exit(position, retrace))
+ self.assertEqual(store[key]["best_rate"], best)
+ self.now += datetime.timedelta(minutes=5)
+ self.frame.loc[self.frame.index[-1], "date"] = self.now
+ self.assertEqual(self.exit(position, retrace), tag)
+
+ def test_invalid_final_exit_state_is_cleared_then_rearmed(self):
+ key = QuickAdapterV3._FINAL_TAKE_PROFIT_STATE_KEY
+ position = self.position(exit_stage=3)
+ store = self.custom_data(position, {key: {"best_rate": "corrupt"}})
+ self.assertIsNone(self.exit(position, 100.0))
+ self.assertIsNone(store[key])
+ self.assertIsNone(self.exit(position, 130.0))
+ self.assertEqual(store[key]["best_rate"], 130.0)
+ self.assertAlmostEqual(store[key]["retracement_distance"], 5.25, places=12)
+
+ def test_an_unmeasurable_final_trail_does_not_arm_or_exit(self):
+ position = self.position(short=True, exit_stage=3)
+ store = self.custom_data(position)
+ # Target 79 is crossed, but a zero current rate cannot seed a positive trail.
+ self.assertIsNone(self.exit(position, 0.0))
+ self.assertNotIn(QuickAdapterV3._FINAL_TAKE_PROFIT_STATE_KEY, store)
+ self.assertEqual(store["history"]["take_profit_price"], [(3, 79.0)])
+
+ def test_a_missing_final_take_profit_target_leaves_state_unarmed(self):
+ position = self.position(exit_stage=3)
+ store = self.custom_data(position)
+ self.frame["natr_label_period_candles"] = np.nan
+ self.assertIsNone(self.exit(position, 150.0))
+ self.assertEqual(store, {})
+
class ThrottleCallbackTest(QaTestCase):
def test_a_non_callable_is_refused(self):
self.assertTrue(np.isfinite(smoothed.to_numpy()).all())
def test_the_labels_and_weights_do_not_depend_on_the_frame_index(self):
- # The strongest form of the positional-contract guard, because it runs the real
- # strategy path. Every other fixture in this suite builds `date` as a COLUMN and
- # leaves a zero-based RangeIndex in place, where a pivot position and a pivot index
- # label are the same number — so returning `df.index` instead of positions passed
- # everywhere. FreqAI hands `set_freqai_targets` a DatetimeIndex frame, where the
- # labels are Timestamps: the weighting path raised TypeError on the first candle of
- # every pair, and a shifted integer index dropped every pivot and returned no weight
- # at all, with only a warning.
+ # Exercise the real strategy path with datetime and shifted integer indexes.
+ # The zero-based fixtures cannot distinguish pivot positions from index labels.
+ # This offset-500 fixture puts every pivot label outside the positional range;
+ # smaller shifts can instead retain weights on incorrect rows. No particular
+ # index type is assumed for native FreqAI inputs.
baseline = None
for name, index in (
("datetime", "date"),
import hashlib
import itertools
import unittest
+from decimal import Decimal, localcontext
import numpy as np
import pandas as pd
)
self.assertAlmostEqual(float(raw[3]), float(closes.iloc[:4].mean()), places=9)
- def test_a_constant_window_gives_a_finite_fractal_dimension_instead_of_a_log_of_zero(self):
- # _fractal_dimension short-circuits to 1.0 when either half-range is zero, and a
- # constant frame is the only shape that reaches that branch. The case was previously
- # named for a gap and a window edge that its fixture cannot contain, which read as if
- # the early return were covered from a second direction. What is actually distinct
- # here is the guard itself: without it the ratio is log(0) and alpha is -inf.
- result = frama(ohlcv_frame([7.0] * 12), 4).to_numpy()
- self.assertFalse(np.isinf(result).any())
- self.assertFalse(np.isnan(result[3:]).any())
+ def test_frama_adapts_to_normalized_window_ranges(self):
+ # Independent high-precision slopes from Ehlers, with this API's close price,
+ # historical window and mean seed. Flat ranges also exercise the zero guard.
+ fixtures = (
+ ("oscillation", [1.0, 3.0] * 12, 0.25),
+ ("trend", list(range(1, 25)), 0.25),
+ ("mixed", [1, 2, 1, 4, 3, 7, 2, 5, 1, 2, 8, 3] * 2, 0.25),
+ ("flat", [7.0] * 24, 0.0),
+ ("flat_halves", [7, 7, 9, 9, 11, 11, 10, 10] * 3, 0.0),
+ )
+ for name, prices, spread in fixtures:
+ frame = pd.DataFrame(
+ {
+ "high": np.asarray(prices) + spread,
+ "low": np.asarray(prices) - spread,
+ "close": prices,
+ },
+ index=pd.Index([f"bar-{i}" for i in range(len(prices))]),
+ )
+ for period in (4, 6, 8):
+ with self.subTest(shape=name, period=period), localcontext() as context:
+ context.prec = 60
+ highs = [Decimal(str(value)) for value in frame["high"]]
+ lows = [Decimal(str(value)) for value in frame["low"]]
+ closes = [Decimal(str(value)) for value in frame["close"]]
+ half = period // 2
+ expected = np.full(len(frame), np.nan)
+ previous = sum(closes[:period]) / Decimal(period)
+ expected[period - 1] = float(previous)
+ for i in range(period, len(frame)):
+ start = i - period
+ split = start + half
+ n1 = (max(highs[start:split]) - min(lows[start:split])) / half
+ n2 = (max(highs[split:i]) - min(lows[split:i])) / half
+ n3 = (max(highs[start:i]) - min(lows[start:i])) / period
+ dimension = (
+ ((n1 + n2).ln() - n3.ln()) / Decimal(2).ln()
+ if n1 + n2 > 0 and n3 > 0
+ else Decimal(1)
+ )
+ dimension = min(Decimal(2), max(Decimal(1), dimension))
+ alpha = (Decimal("-4.6") * (dimension - 1)).exp()
+ alpha = min(Decimal(1), max(Decimal("0.01"), alpha))
+ previous = alpha * closes[i] + (1 - alpha) * previous
+ expected[i] = float(previous)
+ result = frama(frame, period)
+ self.assertTrue(result.index.equals(frame.index))
+ assert_allclose(
+ result.to_numpy(), expected, rtol=1e-12, atol=0.0, equal_nan=True
+ )
def test_the_smma_follows_its_own_recursion(self):
# Seeded with the mean of the first `period` bars, then alpha = 1/period per bar.
series = pd.Series(0.0, index=dataframe.index)
if result.indices:
- # `indices` are POSITIONS and `series` carries the caller's index, so this assignment
- # must be positional. `.loc` would read the positions as labels, which raises KeyError
- # on the DatetimeIndex FreqAI passes and on any shifted integer index, and on a
- # PERMUTED integer index writes the directions onto the wrong rows without raising.
+ # Pivot indices are row positions, independently of the caller's index.
+ # Label-based assignment can fail when labels are absent or place directions
+ # on the wrong rows for a reordered or shifted integer index.
series.iloc[result.indices] = result.directions
metrics: dict[str, list[float]] = {
def _fractal_dimension(
highs: NDArray[np.floating], lows: NDArray[np.floating], period: int
) -> float:
- """Fractal dimension using unnormalized half-window ranges.
+ """Fractal dimension from Ehlers' normalized half-window ranges.
- In exact arithmetic, finite half-window ranges satisfy
- ``HL1 + HL2 <= 2 * HL3``, so clipping gives dimension one. Floating-point
- rounding can leave the dimension slightly above that bound.
-
- Ehlers' normalized formulation divides each half-window range by
- ``period / 2`` and the full-window range by ``period`` before computing
- the dimension. This implementation does not use that normalization.
+ Each half-window range is divided by ``period / 2`` and the full-window
+ range by ``period``. Their lengths cancel to a factor two in the range
+ ratio. Degenerate zero ranges use dimension one; finite dimensions are
+ clipped to ``[1, 2]``.
Reference: https://www.mesasoftware.com/papers/FRAMA.pdf
"""
if period % 2 != 0 or period < 2:
if (HL1 + HL2) == 0 or HL3 == 0:
return 1.0
- D = (np.log(HL1 + HL2) - np.log(HL3)) / np.log(2)
+ # Normalizing the half/full window lengths adds log2(2) to the log ratio.
+ D = 1.0 + (np.log(HL1 + HL2) - np.log(HL3)) / np.log(2)
return np.clip(D, 1.0, 2.0)
def frama(df: pd.DataFrame, period: int = 16, zero_lag: bool = False) -> pd.Series:
- """
- FRAMA-style filter using unnormalized ranges, with optional zero lag.
+ """Fractal adaptive moving average using normalized ranges.
- See :func:`_fractal_dimension` for the distinction from Ehlers' formulation.
+ Filter closes, seed with their first-period mean at ``period - 1``, and
+ estimate each dimension from the preceding ``period`` candles. Optional
+ zero lag preprocesses highs, lows and closes before these operations.
+ See :func:`_fractal_dimension` for the normalized dimension.
"""
if period % 2 != 0:
raise ValueError(f"Invalid period value {period!r}: must be even")
repositories / freqai-strategies.git / commitdiff
