| freqai.optuna_hyperopt.reset_label_study_on_schema_mismatch | true | bool | Reset a persisted `label` study when its selection schema is missing, invalid, or incompatible. `true` performs a destructive reset, deleting the study before recreating it; `false` preserves its trials and stored metadata, permits caller-managed reuse in memory, and does not persist selected params until the schema is reconciled. Both fail closed: an inspection error, or (under `true`) a deletion error, aborts study creation. Has no effect when `continuous=true` or outside live/dry-run modes, where studies are always reset. |
| freqai.optuna_hyperopt.vary_model_seed_by_trial | true | bool | Add `trial.number` to each regressor's configured model seed (or its default seed of `1`) during HPO. `true` samples model randomness across trials; `false` evaluates every trial and the final fit with the same model seed. This does not change `freqai.optuna_hyperopt.seed`. |
+In backtests, continual training uses only a saved model whose training cutoff
+precedes the current window's end and its last available candle boundary. A
+later model left under the same identifier is not reused when a backtest is
+extended into the past. Live and dry-run restarts still restore compatible
+deployed models.
+
The `label_weighting`, `label_smoothing`, `label_pipeline` and
`label_prediction` sections accept either the flat paths listed above or a
per-label format using `default` and `columns.<glob>`. Do not mix both formats in
`shuffle_after_split`. HPO studies and saved best parameters are reused only
when their objective identity matches.
+Backtests continue only from a saved policy whose training cutoff precedes
+the current window's end and its last available candle boundary. A later
+deployment under the same identifier is not reused for an earlier window,
+even when FreqAI has saved only metadata for intervening windows. Live and
+dry-run restarts still restore compatible deployed policies.
+
### Live inference
Optional `fit_live_predictions_candles` statistics use the latest persisted real
position once and clears the terminal potential. `get_env_history()` returns one
metrics/price row per transition. Its `execution_tick` is the transition/action/fill
key before the tick increment; its `tick` is the returned post-increment price and
-observation row (normally `execution_tick + 1`). Ordered trade events remain
-separate in `trade_history`, where each event's `tick` equals the history row's
-`execution_tick`; multiple events may share that key. `terminal_liquidation` and
-`exit_pnl` remain on the transition history row.
+observation row (normally `execution_tick + 1`). Exit-efficiency extrema include
+the fee-adjusted PnL at entry and subsequent retained market marks. Ordered
+trade events remain separate in `trade_history`: their tick identifies the
+candle whose price filled the event. Action fills use `execution_tick`;
+terminal liquidations use the returned post-increment tick. `terminal_liquidation`
+and `exit_pnl` remain on the transition history row.
## Development
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"\n ...",
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"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
"rule": "reportArgumentType",
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"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
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"message": "Operator \"*\" not supported for types \"Any | None\" and \"Any | int | None\"\n Operator \"*\" not supported for types \"None\" and \"None\"\n Operator \"*\" not supported for types \"None\" and \"int\"",
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"message": "Operator \"*\" not supported for types \"Any | None\" and \"Any | int | None\"\n Operator \"*\" not supported for types \"None\" and \"None\"\n Operator \"*\" not supported for types \"None\" and \"int\"",
"rule": "reportOperatorIssue",
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"message": "Operator \">\" not supported for \"None\"",
"rule": "reportOptionalOperand",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"update_best_reward\" is not a known attribute of \"None\"",
"rule": "reportOptionalMemberAccess",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 67,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 67,
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},
{
"endCharacter": 46,
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"is_pruned\" is not a known attribute of \"None\"",
"rule": "reportOptionalMemberAccess",
"severity": "error",
"startCharacter": 37,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"best_mean_reward\" is not a known attribute of \"None\"",
"rule": "reportOptionalMemberAccess",
"severity": "error",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Cannot assign to attribute \"train_env\" for class \"ReforceXY*\"\n Type \"None\" is not assignable to type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\"\n \"None\" is not assignable to \"VecMonitor\"\n \"None\" is not assignable to \"SubprocVecEnv\"\n \"None\" is not assignable to \"Env[Unknown, Unknown]\"",
"rule": "reportAttributeAccessIssue",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Cannot assign to attribute \"eval_env\" for class \"ReforceXY*\"\n Type \"None\" is not assignable to type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\"\n \"None\" is not assignable to \"VecMonitor\"\n \"None\" is not assignable to \"SubprocVecEnv\"\n \"None\" is not assignable to \"Env[Unknown, Unknown]\"",
"rule": "reportAttributeAccessIssue",
"severity": "error",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Declaration \"MyRLEnv\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
"severity": "error",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Declaration \"_last_closed_trade_tick\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Method \"reset\" overrides class \"BaseEnvironment\" in an incompatible manner\n Return type mismatch: base method returns type \"tuple[DataFrame, dict[Unknown, Unknown]]\", override returns type \"tuple[NDArray[float32], dict[str, Any]]\"\n \"tuple[NDArray[float32], dict[str, Any]]\" is not assignable to \"tuple[DataFrame, dict[Unknown, Unknown]]\"\n Tuple entry 1 is incorrect type\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"DataFrame\"",
"rule": "reportIncompatibleMethodOverride",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Type \"tuple[DataFrame, dict[Unknown, Unknown]]\" is not assignable to return type \"tuple[NDArray[float32], dict[str, Any]]\"\n \"DataFrame\" is not assignable to \"ndarray[_AnyShape, dtype[float32]]\"",
"rule": "reportReturnType",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 44,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 44,
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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"message": "Method \"_get_observation\" overrides class \"BaseEnvironment\" in an incompatible manner\n Return type mismatch: base method returns type \"DataFrame\", override returns type \"NDArray[float32]\"\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"DataFrame\"",
"rule": "reportIncompatibleMethodOverride",
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"message": "Method \"step\" overrides class \"Base5ActionRLEnv\" in an incompatible manner\n Return type mismatch: base method returns type \"tuple[DataFrame, float, bool, Literal[False], dict[str, Unknown]]\", override returns type \"tuple[NDArray[float32], float, bool, bool, dict[str, Any]]\"\n \"tuple[NDArray[float32], float, bool, bool, dict[str, Any]]\" is not assignable to \"tuple[DataFrame, float, bool, Literal[False], dict[str, Unknown]]\"\n Tuple entry 1 is incorrect type\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"DataFrame\"",
"rule": "reportIncompatibleMethodOverride",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Method \"action_masks\" overrides class \"BaseEnvironment\" in an incompatible manner\n Return type mismatch: base method returns type \"list[bool]\", override returns type \"NDArray[bool_]\"\n \"ndarray[_AnyShape, dtype[bool_]]\" is not assignable to \"list[bool]\"",
"rule": "reportIncompatibleMethodOverride",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"Figure\" is not exported from module \"matplotlib.pyplot\"",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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"message": "Argument of type \"object\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"object\" is not assignable to type \"ConvertibleToFloat\"\n \"object\" is not assignable to \"str\"\n \"object\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"object\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"object\" is incompatible with protocol \"SupportsIndex\"\n \"__index__\" is not present",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
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provided).
- **`--unrealized_pnl`** (flag, default: false) – Transform the retained
in-position synthetic price/PnL trajectory using fee-aware unrealized PnL.
- This affects extrema and all enabled base, PBRS, and additive reward terms
- that depend on PnL. (Simulation-only.)
+ This affects subsequent retained extrema and enabled reward terms that depend
+ on PnL. (Simulation-only.)
### Hybrid Simulation Scalars
Let `max_u = max_unrealized_profit`, `min_u = min_unrealized_profit`,
`range = max_u - min_u`, `ratio = (pnl - min_u)/range`,
`min_range = max(1e-6, 0.01 · pnl_target)`. Then:
-
- If `range < min_range`: `efficiency_coefficient = 1` (guard against division
explosion)
- If `pnl > 0`:
`efficiency_coefficient = 1 + efficiency_weight · (efficiency_center - ratio)`
- Else: `efficiency_coefficient = 1`
+The extrema start with the fee-adjusted PnL at the entry fill and then include
+each retained market mark. In synthetic `unrealized_pnl` mode, a sampled
+candidate discarded by the transform is not an extremum.
+
##### Exit Attenuation
| Parameter | Default | Description |
trade_duration = 0
idle_duration = 0
entry_open = current_open
- max_unrealized_profit = -np.inf
- min_unrealized_profit = np.inf
elif action == Actions.Short_enter and short_allowed:
position = Positions.Short
trade_duration = 0
idle_duration = 0
entry_open = current_open
- max_unrealized_profit = -np.inf
- min_unrealized_profit = np.inf
+ if position in (Positions.Long, Positions.Short):
+ entry_pnl = _compute_unrealized_pnl_estimate(
+ position, entry_open=entry_open, current_open=entry_open, params=params
+ )
+ max_unrealized_profit = min_unrealized_profit = entry_pnl
else:
idle_duration = 0
if action in (Actions.Long_exit, Actions.Short_exit):
expected_duration = 0
self.assertEqual(row.trade_duration, expected_duration)
- def test_first_retained_pnl_is_only_exit_extremum(self):
- """A first-candle exit excludes the fill-time PnL from its extrema."""
+ def test_synthetic_exit_reward_includes_fee_adjusted_entry_pnl(self):
params = self.base_params(
unrealized_pnl=False,
max_trade_duration_candles=100,
- entry_fee_rate=0.0,
- exit_fee_rate=0.0,
+ entry_fee_rate=0.0015,
+ exit_fee_rate=0.0015,
+ exit_attenuation_mode="linear",
+ exit_plateau=False,
hold_potential_enabled=False,
entry_additive_enabled=False,
exit_additive_enabled=False,
)
actions = [
(Actions.Long_enter, 1.0, 0.0, 0.0),
+ (Actions.Neutral, 0.0, 0.0, 1.0),
(Actions.Long_exit, 0.0, 1.0, 0.0),
]
with (
patch.object(reward_space_analysis, "_sample_action", side_effect=actions),
patch.object(
- reward_space_analysis.random.Random,
- "gauss",
- side_effect=[0.02, 0.0],
+ reward_space_analysis.random.Random, "gauss", side_effect=[0.02, -0.01, 0.0]
),
):
df = simulate_samples(
params=params,
- num_samples=2,
+ num_samples=3,
seed=SEEDS.BASE,
base_factor=PARAMS.BASE_FACTOR,
profit_aim=PARAMS.PROFIT_AIM,
pnl_duration_vol_scale=PARAMS.PNL_DUR_VOL_SCALE,
)
- exit_row = df.iloc[1]
+ entry_pnl = 1.0 / ((1.0 + params["entry_fee_rate"]) * (1.0 + params["exit_fee_rate"])) - 1.0
+ peak_pnl = float(df.iloc[0]["next_pnl"])
+ exit_row = df.iloc[-1]
pnl = float(exit_row["pnl"])
+ self.assertLess(entry_pnl, 0.0)
+ self.assertGreater(peak_pnl, pnl)
self.assertGreater(pnl, 0.0)
- runtime_context = reward_space_analysis.RewardContext(
- current_pnl=pnl,
- trade_duration=int(exit_row["trade_duration"]),
- idle_duration=0,
- max_unrealized_profit=pnl,
- min_unrealized_profit=pnl,
- position=Positions.Long,
- action=Actions.Long_exit,
+ efficiency = 1.0 + params["efficiency_weight"] * (
+ (pnl - entry_pnl) / (peak_pnl - entry_pnl) - params["efficiency_center"]
+ )
+ duration_ratio = float(exit_row["trade_duration"]) / params["max_trade_duration_candles"]
+ expected = (
+ pnl
+ * params["base_factor"]
+ / (1.0 + params["exit_linear_slope"] * duration_ratio)
+ * efficiency
)
- expected_exit = reward_space_analysis.calculate_reward(
- runtime_context,
- params,
- PARAMS.BASE_FACTOR,
- PARAMS.PROFIT_AIM,
- PARAMS.RISK_REWARD_RATIO,
- short_allowed=True,
- action_masking=True,
- ).exit_component
self.assertAlmostEqualFloat(
float(exit_row["reward_exit"]),
- expected_exit,
+ expected,
tolerance=TOLERANCE.IDENTITY_RELAXED,
rtol=TOLERANCE.RELATIVE,
)
)
self.assertTrue(replay["next_pnl"].equals(df["next_pnl"]))
- def test_unrealized_pnl_exit_rewards_use_only_retained_extrema(self):
- """Exit rewards use extrema reconstructed from the retained PnL trajectory."""
+ def test_unrealized_pnl_exit_rewards_include_fill_and_retained_extrema(self):
+ """The fee-adjusted fill and retained prices determine exit efficiency."""
params = self.base_params(
unrealized_pnl=True,
max_trade_duration_candles=100,
Actions.Long_enter,
Actions.Short_enter,
):
- max_unrealized = -np.inf
- min_unrealized = np.inf
+ fee_factor = (1.0 + params["entry_fee_rate"]) * (1.0 + params["exit_fee_rate"])
+ entry_pnl = (
+ 1.0 / fee_factor - 1.0 if action == Actions.Long_enter else 1.0 - fee_factor
+ )
+ max_unrealized = min_unrealized = entry_pnl
if position in (Positions.Long, Positions.Short) and action in (
Actions.Long_exit,
import numpy as np
import pandas as pd
-from ReforceXY.user_data.freqaimodels.ReforceXY import Actions, MyRLEnv
+from ReforceXY.user_data.freqaimodels.ReforceXY import Actions, MyRLEnv, ReforceXY
class PortfolioReturnsTest(unittest.TestCase):
self.assertEqual(info["most_recent_profit"], 0.0)
self.assertAlmostEqual(np.expm1(env.portfolio_log_returns.sum()), env._total_profit - 1)
+ def test_exit_reward_includes_fee_adjusted_entry_extremum(self):
+ for short, mark in ((False, 110.0), (True, 90.0)):
+ with self.subTest(short=short):
+ env = self.make_env([100.0, 100.0, mark, mark, mark])
+ entry = Actions.Short_enter if short else Actions.Long_enter
+ exit_action = Actions.Short_exit if short else Actions.Long_exit
+ fee_factor = (1.0 + env.fee) ** 2
+ entry_pnl = 1.0 - fee_factor if short else 1.0 / fee_factor - 1.0
+ exit_pnl = (
+ 1.0 - mark / 100.0 * fee_factor if short else mark / 100.0 / fee_factor - 1.0
+ )
+
+ env.step(entry.value)
+ self.assertAlmostEqual(env.get_min_unrealized_profit(), entry_pnl)
+ self.assertAlmostEqual(env.get_max_unrealized_profit(), exit_pnl)
+ _, reward, _, _, _ = env.step(exit_action.value)
+
+ duration = 1.0 / env.max_trade_duration_candles
+ if ReforceXY.DEFAULT_EXIT_PLATEAU:
+ duration = max(0.0, duration - ReforceXY.DEFAULT_EXIT_PLATEAU_GRACE)
+ attenuation = 1.0 / (1.0 + ReforceXY.DEFAULT_EXIT_LINEAR_SLOPE * duration)
+ target_coefficient = 1.0
+ if exit_pnl > env._pnl_target:
+ target_coefficient += ReforceXY.DEFAULT_WIN_REWARD_FACTOR * math.tanh(
+ ReforceXY.DEFAULT_PNL_AMPLIFICATION_SENSITIVITY
+ * (exit_pnl / env._pnl_target - 1.0)
+ )
+ efficiency = 1.0 + ReforceXY.DEFAULT_EFFICIENCY_WEIGHT * (
+ 1.0 - ReforceXY.DEFAULT_EFFICIENCY_CENTER
+ )
+ self.assertAlmostEqual(
+ reward,
+ exit_pnl
+ * ReforceXY.DEFAULT_BASE_FACTOR
+ * attenuation
+ * target_coefficient
+ * efficiency,
+ places=9,
+ )
+
def test_transitions_match_equity_for_both_staking_modes_and_directions(self):
prices = [100.0, 100.0, 110.0, 105.0, 115.0, 120.0, 118.0, 117.0, 117.0]
for short in (False, True):
env.trade_history[-1]["type"],
"short_exit" if short else "long_exit",
)
- self.assertEqual(env.trade_history[-1]["tick"], info["execution_tick"])
self.assertEqual(env.trade_history[-1]["price"], terminal_price)
self.assertAlmostEqual(env.trade_history[-1]["profit"], expected_pnl)
self.assertAlmostEqual(
else:
pd.testing.assert_frame_equal(history, baseline)
- def test_terminal_entry_preserves_two_events_and_plot_markers(self):
+ def test_terminal_fill_events_and_plot_match_their_price_candles(self):
env = self.make_env([100.0, 100.0, 90.0])
_, _, terminated, truncated, info = env.step(Actions.Long_enter.value)
expected_pnl = 90.0 / 100.0 / (1.0 + env.fee) ** 2 - 1.0
self.assertEqual(history.iloc[0]["execution_tick"], info["execution_tick"])
self.assertEqual(history.iloc[0]["open"], 90.0)
- self.assertEqual(len(env.trade_history), 2)
- self.assertEqual(
- [(event["tick"], event["type"]) for event in env.trade_history],
- [(info["execution_tick"], "long_enter"), (info["execution_tick"], "long_exit")],
- )
- self.assertEqual(
- [event["price"] for event in env.trade_history],
- [100.0, 90.0],
- )
- self.assertEqual(env.trade_history[0]["profit"], 0.0)
- self.assertAlmostEqual(env.trade_history[1]["profit"], expected_pnl)
+ events = env.trade_history
+ self.assertEqual([event["type"] for event in events], ["long_enter", "long_exit"])
+ for event in events:
+ self.assertAlmostEqual(event["price"], env.prices.iloc[event["tick"]]["open"])
+ self.assertEqual(events[0]["profit"], 0.0)
+ self.assertAlmostEqual(events[1]["profit"], expected_pnl)
figure = env.get_env_plot()
- marker_lines = [
+ curve = figure.axes[0].lines[0]
+ markers = [
line
for line in figure.axes[0].lines
if line.get_linestyle() == "None" and len(line.get_xdata()) == 1
]
- self.assertEqual([line.get_marker() for line in marker_lines], ["^", "."])
- self.assertEqual([line.get_xdata()[0] for line in marker_lines], [1, 1])
+ self.assertEqual([line.get_marker() for line in markers], ["^", "."])
+ self.assertEqual(
+ [(line.get_xdata()[0], line.get_ydata()[0]) for line in markers],
+ [(event["tick"], event["price"]) for event in events],
+ )
+ self.assertEqual(
+ (markers[-1].get_xdata()[0], markers[-1].get_ydata()[0]),
+ (curve.get_xdata()[-1], curve.get_ydata()[-1]),
+ )
def test_nonpositive_equity_is_not_reported_as_zero_return(self):
env = self.make_env([100.0, 100.0, 100.0, 300.0, 300.0], fee=0.0)
)
self.assertAlmostEqual(entry_reward, env._potential_gamma * expected_potential)
- _, exit_reward, exit_done, exit_truncated, _ = env.step(2)
+ _, _, exit_done, exit_truncated, _ = env.step(2)
self.assertFalse(exit_done)
self.assertFalse(exit_truncated)
self.assertEqual(env.trade_history[-1]["tick"], 3)
self.assertEqual(env.trade_history[-1]["price"], 110.0)
- pnl_coefficient = 1.0 + 2.0 * math.tanh(2.0 * (observed_pnl / env._pnl_target - 1.0))
- # The entry transition retains one PnL observation, so max equals min
- # and the independent efficiency formula remains at its neutral value 1.0.
- expected_base_exit = observed_pnl * ReforceXY.DEFAULT_BASE_FACTOR * pnl_coefficient
- self.assertAlmostEqual(exit_reward, expected_base_exit - expected_potential, places=5)
env.step(1)
_, _, done, truncated, info = env.step(0)
import numpy as np
import pandas as pd
+from freqtrade.enums import RunMode
from freqtrade.exceptions import DependencyException
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
)
self.assertTrue(np.isfinite(score))
+ def test_backtest_rejects_future_archive_but_continues_from_earlier_saved_window(self):
+ with tempfile.TemporaryDirectory() as temp:
+ config = model_config(temp)
+ info = config["freqai"]
+ info["continual_learning"] = True
+ info["rl_config"]["model_type"] = "DQN"
+ info["model_training_parameters"] = {
+ "learning_starts": 0,
+ "buffer_size": 128,
+ "batch_size": 8,
+ "train_freq": 4,
+ "gradient_steps": 1,
+ "device": "cpu",
+ "policy_kwargs": {"net_arch": [8]},
+ }
+ pair = "BTC/USDT"
+
+ def frame(day, feature_offset):
+ values = np.arange(64)
+ data = pd.DataFrame(
+ {
+ "date": pd.date_range(day, periods=64, freq="5min", tz="UTC"),
+ "%-feature": np.sin(values) + feature_offset,
+ "&-action": np.zeros(64),
+ }
+ )
+ for column in ("open", "high", "low", "close"):
+ data[f"%-raw_{column}"] = 100.0 + values * 0.1
+ return data
+
+ def kitchen(settings, data, *, live):
+ dk = FreqaiDataKitchen(settings, live=live, pair=pair)
+ timestamp = int((data["date"].iloc[-1] + pd.Timedelta(minutes=5)).timestamp())
+ dk.set_paths(pair, timestamp)
+ dk.set_new_model_names(pair, timestamp)
+ dk.data_path.mkdir(parents=True, exist_ok=True)
+ dk.label_list = ["&-action"]
+ dk.training_features_list = [column for column in data if column.startswith("%")]
+ return dk, timestamp
+
+ source = ReforceXY(config=config)
+ source.live = True
+ source.can_short = False
+ self.addCleanup(source.close_envs)
+ future = frame("2026-02-01", 1000.0)
+ future_dk, future_ts = kitchen(config, future, live=True)
+ deployed = source.train(future, pair, future_dk)
+ source.dd.get_pair_dict_info(pair)
+ source.dd.pair_dict[pair]["trained_timestamp"] = future_ts
+ source.dd.save_data(deployed, pair, future_dk)
+
+ backtest_config = dict(config)
+ backtest_config["runmode"] = RunMode.BACKTEST
+ backtest_config["timerange"] = "20260101-20260105"
+ backtest_config["config_files"] = [
+ "/workspace/ReforceXY/user_data/config-template.json"
+ ]
+ backtest = ReforceXY(config=backtest_config)
+ backtest.live = False
+ backtest.can_short = False
+ self.addCleanup(backtest.close_envs)
+ for day, offset, save_model in (
+ ("2026-01-01", 0.0, False),
+ ("2026-01-02", 10.0, True),
+ ("2026-01-03", 20.0, False),
+ ):
+ training_frame = frame(day, offset)
+ dk, timestamp = kitchen(backtest_config, training_frame, live=False)
+ self.assertLess(timestamp, future_ts)
+ self.assertFalse(backtest.model_exists(dk))
+ trained = backtest.train(training_frame, pair, dk)
+ transformed = dk.data_dictionary["train_features"]["%-feature"]
+ if offset < 20.0:
+ self.assertAlmostEqual(transformed.min(), -1.0)
+ self.assertAlmostEqual(transformed.max(), 1.0)
+ else:
+ self.assertGreater(transformed.min(), 2.0)
+ backtest.dd.pair_dict[pair]["trained_timestamp"] = timestamp
+ if save_model:
+ backtest.dd.save_data(trained, pair, dk)
+ else:
+ backtest.dd.save_metadata(dk)
+
def test_provenance_does_not_change_other_drawers(self):
with tempfile.TemporaryDirectory() as temp:
config = model_config(temp)
return callbacks
def _resolve_deployment_state(
- self, dk: FreqaiDataKitchen, pair: str
+ self, dk: FreqaiDataKitchen, pair: str, *, as_of_ts: int | None = None
) -> tuple[Any, Pipeline] | None:
"""Restore independent training copies of the deployed policy and feature pipeline."""
if not self.continual_learning:
previous = self.dd.pair_dict.get(pair, {})
if model is None and not previous.get("model_filename"):
return None
+ if not self.live:
+ # The drawer timestamp may advance without saving a new policy.
+ # Bound the actual artifact before loading policy, pipeline or replay.
+ name, separator, timestamp = previous.get("model_filename", "").rpartition("_")
+ if (
+ as_of_ts is None
+ or not previous.get("trained_timestamp")
+ or not separator
+ or name != f"cb_{pair.split('/')[0].lower()}"
+ or not timestamp.isdecimal()
+ or int(timestamp) >= as_of_ts
+ ):
+ return None
try:
cached = self.dd.meta_data_dictionary.get(pair, {})
metadata = cached.get(METADATA)
dk.fit_labels()
# Capture prices once, before normalization and optional raw-OHLC removal.
prices_train, prices_test = self.build_ohlc_price_dataframes(raw_data, pair, dk)
- deployment_state = self._resolve_deployment_state(dk, pair)
+ as_of_ts = None
+ if self.continual_learning and not self.live:
+ latest_date = pd.to_datetime(unfiltered_df["date"], utc=True).max()
+ as_of_ts = int(latest_date.timestamp()) + 60 * timeframe_to_minutes(
+ self.config["timeframe"]
+ )
+ training_timerange = getattr(self, "training_timerange", None)
+ if training_timerange is not None:
+ as_of_ts = min(as_of_ts, int(training_timerange.stopts))
+ deployment_state = self._resolve_deployment_state(dk, pair, as_of_ts=as_of_ts)
dd = self._apply_training_pipeline(
raw_data, dk, deployment_state=None if self.hyperopt else deployment_state
)
reward = self.calculate_reward(action)
trade_type = self.execute_trade(action)
entry_pnl = self.get_unrealized_profit() if previous_position == Positions.Neutral else 0.0
+ if trade_type is not None and self._position in (Positions.Long, Positions.Short):
+ self._update_max_unrealized_profit(entry_pnl)
+ self._update_min_unrealized_profit(entry_pnl)
if trade_type is not None:
self.append_trade_history(
trade_type, self.current_price(), pre_pnl, execution_tick=execution_tick
f"{closed_position.name}_exit",
self.current_price(),
terminal_pnl,
- execution_tick=execution_tick,
+ execution_tick=self._current_tick,
)
if terminated:
reward = self._apply_terminal_pbrs_correction(reward)
},
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"message": "Cannot access attribute \"empty\" for class \"Buffer\"\n Attribute \"empty\" is unknown",
"rule": "reportAttributeAccessIssue",
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"severity": "error",
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},
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"rule": "reportOptionalMemberAccess",
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},
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},
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},
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},
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"rule": "reportArgumentType",
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},
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"severity": "error",
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},
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def _resolve_deployment_state(
- self, dk: FreqaiDataKitchen, pair: str
+ self, dk: FreqaiDataKitchen, pair: str, *, as_of_ts: int | None = None
) -> tuple[Any, Pipeline, Pipeline] | None:
"""Restore the deployed model with independent copies of its fitted pipelines."""
if not self.continual_learning:
previous = self.dd.pair_dict.get(pair, {})
if model is None and not previous.get("model_filename"):
return None
+ if not self.live:
+ # The drawer timestamp can advance without saving a model. Bound the
+ # actual artifact instead, before reading cached or persisted state.
+ name, separator, timestamp = previous.get("model_filename", "").rpartition("_")
+ if (
+ as_of_ts is None
+ or not previous.get("trained_timestamp")
+ or not separator
+ or name != f"cb_{pair.split('/')[0].lower()}"
+ or not timestamp.isdecimal()
+ or int(timestamp) >= as_of_ts
+ ):
+ return None
try:
cached = self.dd.meta_data_dictionary.get(pair)
if model is not None and cached is not None:
)
if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels()
- deployment_state = self._resolve_deployment_state(dk, pair)
+ as_of_ts = None
+ if self.continual_learning and not self.live:
+ as_of_ts = int(dates.max().timestamp()) + timeframe_to_seconds(self.config["timeframe"])
+ training_timerange = getattr(self, "training_timerange", None)
+ if training_timerange is not None:
+ as_of_ts = min(as_of_ts, int(training_timerange.stopts))
+ deployment_state = self._resolve_deployment_state(dk, pair, as_of_ts=as_of_ts)
dd = self._apply_pipelines(
dd,
train_weight_inputs,
repositories / freqai-strategies.git / commitdiff
