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Use Kelly allocation for Torch position scaling

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This commit is contained in:
Codex
2026-06-26 20:20:23 +03:00
parent 87cb7e8fe3
commit 61a063cda7
12 changed files with 239 additions and 62 deletions
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.env
+4 -4
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@@ -33,11 +33,11 @@ LEARNING_MIN_SAMPLES=3
LEARNING_MAX_ADJUSTMENT=0.12
LEARNING_MAX_POSITION_MULTIPLIER=1.6
MIN_POSITION_USDT=1
MAX_POSITION_USDT=25
MAX_POSITION_USDT=8
MAX_SYMBOL_EXPOSURE_USDT=25
MAX_TOTAL_EXPOSURE_USDT=75
MAX_OPEN_POSITIONS=4
MAX_POSITIONS_PER_SYMBOL=1
MAX_OPEN_POSITIONS=24
MAX_POSITIONS_PER_SYMBOL=6
GRID_TRADING_ENABLED=false
GRID_ENTRY_CONFIDENCE=0.58
GRID_BUY_ZONE=0.45
@@ -46,7 +46,7 @@ REBOUND_TRADING_ENABLED=true
REBOUND_ENTRY_CONFIDENCE=0.55
REBOUND_MIN_PROBABILITY=0.55
REBOUND_MAX_POSITION_USDT=6
KELLY_SIZING_ENABLED=false
KELLY_SIZING_ENABLED=true
KELLY_FRACTION=0.25
KELLY_MAX_FRACTION=0.20
RISK_PER_TRADE_PERCENT=0.01
.env.example
+6 -6
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@@ -33,11 +33,11 @@ LEARNING_MIN_SAMPLES=3
LEARNING_MAX_ADJUSTMENT=0.12
LEARNING_MAX_POSITION_MULTIPLIER=1.6
MIN_POSITION_USDT=1
MAX_POSITION_USDT=25
MAX_POSITION_USDT=8
MAX_SYMBOL_EXPOSURE_USDT=25
MAX_TOTAL_EXPOSURE_USDT=75
MAX_OPEN_POSITIONS=4
MAX_POSITIONS_PER_SYMBOL=1
MAX_OPEN_POSITIONS=24
MAX_POSITIONS_PER_SYMBOL=6
GRID_TRADING_ENABLED=false
GRID_ENTRY_CONFIDENCE=0.58
GRID_BUY_ZONE=0.45
@@ -46,7 +46,7 @@ REBOUND_TRADING_ENABLED=true
REBOUND_ENTRY_CONFIDENCE=0.55
REBOUND_MIN_PROBABILITY=0.55
REBOUND_MAX_POSITION_USDT=6
KELLY_SIZING_ENABLED=false
KELLY_SIZING_ENABLED=true
KELLY_FRACTION=0.25
KELLY_MAX_FRACTION=0.20
RISK_PER_TRADE_PERCENT=0.01
@@ -61,8 +61,8 @@ TREND_RSI_MAX=65
TIME_SERIES_FORECAST_ENABLED=true
TIME_SERIES_MIN_CANDLES=120
TIME_SERIES_FORECAST_HORIZON=3
TIME_SERIES_MIN_EDGE_PERCENT=0.08
TIME_SERIES_MIN_PROBABILITY_UP=0.58
TIME_SERIES_MIN_EDGE_PERCENT=0.10
TIME_SERIES_MIN_PROBABILITY_UP=0.47
TIME_SERIES_MIN_CONFIDENCE=0.4
TIME_SERIES_MAX_ADJUSTMENT=0.08
TIME_SERIES_LSTM_ENABLED=true
README.md
+8 -8
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@@ -5,7 +5,7 @@ Spot-бот для демо-торговли криптовалютой на р
## Что реализовано
- Реальные market data Bybit Spot: REST bootstrap и WebSocket-обновления.
- Фиксированный набор USDT spot-пар для основной стратегии: `BTCUSDT`, `ETHUSDT`, `SOLUSDT`, `LTCUSDT`.
- Фиксированный набор 12 USDT spot-пар для основной стратегии: `BTCUSDT`, `ETHUSDT`, `HYPEUSDT`, `SOLUSDT`, `XRPUSDT`, `XPLUSDT`, `WLDUSDT`, `MNTUSDT`, `HUSDT`, `XAUTUSDT`, `IPUSDT`, `AAVEUSDT`.
- Paper trading с учетом cash, комиссий, проскальзывания, stop-loss, take-profit и trailing stop.
- Spot-only логика: покупка базовой монеты за USDT и продажа обратно, без short и без плеча.
- Live spot-ордеры явно отправляются без плеча: `category=spot`, `isLeverage=0`.
@@ -88,7 +88,7 @@ powershell -ExecutionPolicy Bypass -File tools\run_torch_retrain.ps1
powershell -ExecutionPolicy Bypass -File tools\install_windows_torch_retrainer.ps1
```
По умолчанию Windows-расписание переобучает PyTorch `LSTM/GRU` каждые 6 часов с `--limit 3000` на парах `BTCUSDT,ETHUSDT,SOLUSDT,LTCUSDT`. Параметры можно переопределить через env: `TORCH_RETRAIN_SYMBOLS`, `TORCH_RETRAIN_LIMIT`, `TORCH_RETRAIN_LOOKBACKS`, `TORCH_RETRAIN_ARCHITECTURES`, `TORCH_RETRAIN_HIDDEN_SIZES`, `TORCH_RETRAIN_LAYERS`, `TORCH_RETRAIN_DROPOUTS`, `TORCH_RETRAIN_HORIZON`, `TORCH_RETRAIN_HORIZONS`, `TORCH_RETRAIN_CONTEXT_SYMBOLS`, `TORCH_RETRAIN_FEATURES`, `TORCH_RETRAIN_SEED`, `TORCH_RETRAIN_EPOCHS`, `TORCH_RETRAIN_PATIENCE`, `TORCH_RETRAIN_INTERVAL`, `TORCH_RETRAIN_ENV`.
По умолчанию Windows-расписание переобучает PyTorch `LSTM/GRU` каждые 6 часов с `--limit 3000` на 12 spot-парах из `SYMBOLS`. Параметры можно переопределить через env: `TORCH_RETRAIN_SYMBOLS`, `TORCH_RETRAIN_LIMIT`, `TORCH_RETRAIN_LOOKBACKS`, `TORCH_RETRAIN_ARCHITECTURES`, `TORCH_RETRAIN_HIDDEN_SIZES`, `TORCH_RETRAIN_LAYERS`, `TORCH_RETRAIN_DROPOUTS`, `TORCH_RETRAIN_HORIZON`, `TORCH_RETRAIN_HORIZONS`, `TORCH_RETRAIN_CONTEXT_SYMBOLS`, `TORCH_RETRAIN_FEATURES`, `TORCH_RETRAIN_SEED`, `TORCH_RETRAIN_EPOCHS`, `TORCH_RETRAIN_PATIENCE`, `TORCH_RETRAIN_INTERVAL`, `TORCH_RETRAIN_ENV`.
Если retrain запускается с `-DeployToPi`, после успешного guard он синхронизирует `runtime/lstm_forecaster.json`, `runtime/torch_retrain_guard.json` и `runtime/torch_threshold_calibration.json` на Raspberry Pi через SSH-ключ и перезапускает сервис `tradebot`. Отдельный запуск sync:
@@ -137,11 +137,11 @@ LEARNING_MIN_SAMPLES=3
LEARNING_MAX_ADJUSTMENT=0.12
LEARNING_MAX_POSITION_MULTIPLIER=1.6
MIN_POSITION_USDT=1
MAX_POSITION_USDT=25
MAX_POSITION_USDT=8
MAX_SYMBOL_EXPOSURE_USDT=25
MAX_TOTAL_EXPOSURE_USDT=75
MAX_OPEN_POSITIONS=4
MAX_POSITIONS_PER_SYMBOL=1
MAX_OPEN_POSITIONS=24
MAX_POSITIONS_PER_SYMBOL=6
GRID_TRADING_ENABLED=false
GRID_ENTRY_CONFIDENCE=0.58
GRID_BUY_ZONE=0.45
@@ -150,7 +150,7 @@ REBOUND_TRADING_ENABLED=true
REBOUND_ENTRY_CONFIDENCE=0.55
REBOUND_MIN_PROBABILITY=0.55
REBOUND_MAX_POSITION_USDT=6
KELLY_SIZING_ENABLED=false
KELLY_SIZING_ENABLED=true
KELLY_FRACTION=0.25
KELLY_MAX_FRACTION=0.20
RISK_PER_TRADE_PERCENT=0.01
@@ -165,8 +165,8 @@ TREND_RSI_MAX=65
TIME_SERIES_FORECAST_ENABLED=true
TIME_SERIES_MIN_CANDLES=120
TIME_SERIES_FORECAST_HORIZON=3
TIME_SERIES_MIN_EDGE_PERCENT=0.08
TIME_SERIES_MIN_PROBABILITY_UP=0.58
TIME_SERIES_MIN_EDGE_PERCENT=0.10
TIME_SERIES_MIN_PROBABILITY_UP=0.47
TIME_SERIES_MIN_CONFIDENCE=0.4
TIME_SERIES_MAX_ADJUSTMENT=0.08
TIME_SERIES_LSTM_ENABLED=true
crypto_spot_bot/bot.py
+3
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@@ -156,6 +156,9 @@ class CryptoSpotBot:
learning["adaptive_rules"] = self._with_exposure_context(learning.get("adaptive_rules") or {})
account = self.broker.account_state(prices)
account["risk_guard"] = risk_guard
account["symbol"] = symbol
account["symbol_exposure_usdt"] = self.broker.symbol_exposure(symbol)
account["open_positions_for_symbol"] = open_count
if risk_guard.get("block_new_entries"):
self.storage.insert_signal(
Signal(
crypto_spot_bot/config.py
+14 -1
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@@ -5,7 +5,20 @@ from dataclasses import dataclass
from pathlib import Path
FIXED_SPOT_SYMBOLS = ("BTCUSDT", "ETHUSDT", "SOLUSDT", "LTCUSDT")
FIXED_SPOT_SYMBOLS = (
"BTCUSDT",
"ETHUSDT",
"HYPEUSDT",
"SOLUSDT",
"XRPUSDT",
"XPLUSDT",
"WLDUSDT",
"MNTUSDT",
"HUSDT",
"XAUTUSDT",
"IPUSDT",
"AAVEUSDT",
)
STRATEGY_MODES = {"legacy", "trend_macd", "torch_forecast"}
crypto_spot_bot/execution.py
+10 -4
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@@ -94,10 +94,7 @@ class PaperBroker:
return False, "достигнут общий лимит открытых позиций"
if self.settings.strategy_mode == "trend_macd" and len(self.positions_for_symbol(symbol)) >= 1:
return False, "DCA/усреднение отключено: позиция по паре уже открыта"
dynamic_pair_limit = max(
self.settings.max_positions_per_symbol,
int(self.settings.max_symbol_exposure_usdt // max(self.settings.min_position_usdt, 0.01)),
)
dynamic_pair_limit = _symbol_position_limit(self.settings)
if len(self.positions_for_symbol(symbol)) >= dynamic_pair_limit:
return False, "достигнут лимит позиций по паре"
requested = requested_notional if requested_notional is not None else self.settings.min_position_usdt
@@ -384,3 +381,12 @@ class LiveBroker(PaperBroker):
def prices_from_tickers(tickers: Iterable[Ticker]) -> dict[str, float]:
return {ticker.symbol: ticker.last_price for ticker in tickers}
def _symbol_position_limit(settings: Settings) -> int:
configured_limit = max(1, settings.max_positions_per_symbol)
exposure_based_limit = max(
1,
int(settings.max_symbol_exposure_usdt // max(settings.min_position_usdt, 0.01)),
)
return min(configured_limit, exposure_based_limit)
crypto_spot_bot/market_data.py
+14 -1
View File
@@ -16,7 +16,20 @@ from crypto_spot_bot.models import Candle, Ticker, utc_now
from crypto_spot_bot.storage import Storage
POPULAR_FALLBACK = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "LTCUSDT"]
POPULAR_FALLBACK = [
"BTCUSDT",
"ETHUSDT",
"HYPEUSDT",
"SOLUSDT",
"XRPUSDT",
"XPLUSDT",
"WLDUSDT",
"MNTUSDT",
"HUSDT",
"XAUTUSDT",
"IPUSDT",
"AAVEUSDT",
]
def _float(value: Any, default: float = 0.0) -> float:
crypto_spot_bot/strategy.py
+62 -14
View File
@@ -632,7 +632,7 @@ def _torch_forecast_entry_signal(
return Signal(symbol, "HOLD", 0.0, "torch_forecast: symbol position limit reached")
stop_loss_percent = _clamp(settings.stop_loss_percent, 0.003, 0.08)
sizing = _torch_forecast_position_sizing(settings, account, stop_loss_percent, forecast)
sizing = _torch_forecast_position_sizing(settings, account, stop_loss_percent, forecast, symbol)
position_notional = float(sizing["notional_usdt"])
expected_return = _safe_float(forecast.get("expected_return_percent"), 0.0)
probability_up = _safe_float(forecast.get("probability_up"), 0.5)
@@ -721,6 +721,8 @@ def _torch_forecast_entry_signal(
"rebound_probability": rebound.get("probability", 0.0),
}
edge_mode = "rebound_fallback" if fallback_rebound_entry_ok else "rebound"
risk_size_ok = position_notional >= settings.min_position_usdt
rebound_entry_sized_ok = rebound_entry_ok and risk_size_ok
checks = {
"torch_model_ok": model_ok,
"quality_gate_ok": quality_gate_ok,
@@ -732,7 +734,7 @@ def _torch_forecast_entry_signal(
"confidence_ok": confidence >= settings.time_series_min_confidence,
"spread_ok": spread_ok,
"liquidity_ok": liquidity_ok,
"risk_size_ok": position_notional >= settings.min_position_usdt,
"risk_size_ok": risk_size_ok,
}
diagnostics = {
"strategy_mode": "torch_forecast",
@@ -756,6 +758,7 @@ def _torch_forecast_entry_signal(
"model_rebound_entry_ok": model_rebound_entry_ok,
"fallback_rebound_entry_ok": fallback_rebound_entry_ok,
"rebound_entry_ok": rebound_entry_ok,
"rebound_entry_sized_ok": rebound_entry_sized_ok,
"min_confidence": settings.time_series_min_confidence,
"skill": skill,
"quality_gate": forecast.get("quality_gate", {}),
@@ -769,8 +772,8 @@ def _torch_forecast_entry_signal(
"llm": {},
}
base_entry_ok = all(checks.values())
if base_entry_ok or rebound_entry_ok:
buy_confidence = max(confidence, float(rebound.get("probability", 0.0) or 0.0)) if rebound_entry_ok else confidence
if base_entry_ok or rebound_entry_sized_ok:
buy_confidence = max(confidence, float(rebound.get("probability", 0.0) or 0.0)) if rebound_entry_sized_ok else confidence
entry_path = edge_mode if rebound_entry_ok and not base_entry_ok else edge_mode
diagnostics["entry_path"] = entry_path
if fallback_rebound_entry_ok and not base_entry_ok:
@@ -961,8 +964,12 @@ def _torch_min_probability(settings: Settings) -> float:
def _dynamic_symbol_position_limit(settings: Settings) -> int:
exposure_based_limit = int(settings.max_symbol_exposure_usdt // max(settings.min_position_usdt, 0.01))
return max(1, settings.max_positions_per_symbol, exposure_based_limit)
configured_limit = max(1, settings.max_positions_per_symbol)
exposure_based_limit = max(
1,
int(settings.max_symbol_exposure_usdt // max(settings.min_position_usdt, 0.01)),
)
return min(configured_limit, exposure_based_limit)
def _torch_forecast_confidence(settings: Settings, forecast: dict) -> float:
@@ -982,13 +989,18 @@ def _torch_forecast_position_sizing(
account: dict | None,
stop_loss_percent: float,
forecast: dict,
symbol: str | None = None,
) -> dict[str, float | str]:
base = _trend_position_sizing(settings, account, stop_loss_percent)
base_notional = float(base["notional_usdt"])
if base_notional <= 0:
notional = 0.0
edge_multiplier = probability_multiplier = skill_multiplier = 0.0
else:
kelly = _kelly_position(
settings=settings,
final_score=_torch_forecast_confidence(settings, forecast),
forecast=forecast,
adaptive={},
account=account,
symbol=symbol,
)
expected_return = max(0.0, _safe_float(forecast.get("expected_return_percent"), 0.0))
probability_up = _safe_float(forecast.get("probability_up"), 0.5)
skill = max(0.0, _safe_float(forecast.get("skill"), 0.0))
@@ -996,16 +1008,24 @@ def _torch_forecast_position_sizing(
edge_multiplier = _clamp(expected_return / max(min_edge * 3.0, 0.01), 0.25, 1.15)
probability_multiplier = _clamp(0.75 + (probability_up - 0.55) * 3.0, 0.50, 1.20)
skill_multiplier = _clamp(0.85 + skill * 0.60, 0.60, 1.15)
if settings.kelly_sizing_enabled:
raw = float(kelly["kelly_remaining_notional_usdt"]) * _risk_guard_multiplier(account)
notional = 0.0 if raw < settings.min_position_usdt else min(raw, settings.max_position_usdt)
elif base_notional <= 0:
notional = 0.0
else:
raw = base_notional * edge_multiplier * probability_multiplier * skill_multiplier
notional = 0.0 if raw < settings.min_position_usdt else min(raw, settings.max_position_usdt)
return {
**base,
"method": "torch_forecast_risk",
"method": "torch_forecast_fractional_kelly" if settings.kelly_sizing_enabled else "torch_forecast_risk",
"enabled": bool(settings.kelly_sizing_enabled),
"notional_usdt": round(notional, 2),
"base_notional_usdt": base["notional_usdt"],
"torch_edge_multiplier": round(edge_multiplier, 4),
"torch_probability_multiplier": round(probability_multiplier, 4),
"torch_skill_multiplier": round(skill_multiplier, 4),
**kelly,
}
@@ -1168,6 +1188,7 @@ def _kelly_position(
forecast: dict,
adaptive: dict,
account: dict | None,
symbol: str | None = None,
) -> dict[str, float | bool | str]:
confidence_probability = _confidence_probability(final_score, settings.min_signal_confidence)
probability_source = "confidence"
@@ -1180,8 +1201,13 @@ def _kelly_position(
stop_loss = _adaptive_percent(adaptive, "stop_loss_percent", settings.stop_loss_percent, 0.003, 0.08)
take_profit = _adaptive_percent(adaptive, "take_profit_percent", settings.take_profit_percent, 0.003, 0.20)
round_trip_cost = max(0.0, 2.0 * (settings.taker_fee_rate + settings.slippage_rate))
win_return = max(0.0, take_profit - round_trip_cost)
base_win_return = max(0.0, take_profit - round_trip_cost)
loss_return = max(0.0001, stop_loss + round_trip_cost)
expected_net_return = max(0.0, _safe_float(forecast.get("expected_return_percent"), 0.0) / 100.0)
implied_win_return = 0.0
if probability > 0:
implied_win_return = max(0.0, (expected_net_return + (1.0 - probability) * loss_return) / probability)
win_return = max(base_win_return, implied_win_return)
reward_loss_ratio = win_return / loss_return if loss_return > 0 else 0.0
full_kelly = probability - ((1.0 - probability) / reward_loss_ratio) if reward_loss_ratio > 0 else 0.0
full_kelly = max(0.0, full_kelly)
@@ -1190,19 +1216,41 @@ def _kelly_position(
bankroll = _safe_float((account or {}).get("equity"), settings.starting_balance_usdt)
if bankroll <= 0:
bankroll = settings.starting_balance_usdt
kelly_notional = max(0.0, bankroll * effective_fraction)
target_notional = max(0.0, bankroll * effective_fraction)
open_symbol_exposure = _account_symbol_exposure(account, symbol)
remaining_notional = max(0.0, target_notional - open_symbol_exposure)
return {
"kelly_probability": round(probability, 4),
"kelly_probability_source": probability_source,
"kelly_reward_loss_ratio": round(reward_loss_ratio, 4),
"kelly_win_return_percent": round(win_return * 100.0, 4),
"kelly_loss_return_percent": round(loss_return * 100.0, 4),
"kelly_expected_net_percent": round(expected_net_return * 100.0, 4),
"kelly_full_fraction": round(full_kelly, 4),
"kelly_fractional_fraction": round(fractional_kelly, 4),
"kelly_effective_fraction": round(effective_fraction, 4),
"kelly_bankroll_usdt": round(bankroll, 2),
"kelly_notional_usdt": round(kelly_notional, 2),
"kelly_target_notional_usdt": round(target_notional, 2),
"kelly_open_symbol_exposure_usdt": round(open_symbol_exposure, 2),
"kelly_remaining_notional_usdt": round(remaining_notional, 2),
"kelly_notional_usdt": round(remaining_notional, 2),
}
def _account_symbol_exposure(account: dict | None, symbol: str | None = None) -> float:
if not isinstance(account, dict):
return 0.0
direct = _safe_float(account.get("symbol_exposure_usdt"), -1.0)
if direct >= 0:
return max(0.0, direct)
if not symbol:
symbol = str(account.get("symbol", "") or "")
exposures = account.get("symbol_exposures")
if isinstance(exposures, dict) and symbol:
return max(0.0, _safe_float(exposures.get(symbol), 0.0))
return 0.0
def _confidence_probability(final_score: float, min_signal_confidence: float) -> float:
denominator = max(0.0001, 1.0 - min_signal_confidence)
ratio = _clamp((final_score - min_signal_confidence) / denominator, 0.0, 1.0)
tests/test_execution.py
+28 -1
View File
@@ -60,7 +60,7 @@ def test_paper_broker_uses_signal_notional_and_pair_exposure(make_settings, tmp_
max_symbol_exposure_usdt=6,
max_total_exposure_usdt=50,
max_open_positions=20,
max_positions_per_symbol=1,
max_positions_per_symbol=6,
max_entries_per_minute=0,
)
storage = Storage(settings.database_path)
@@ -218,6 +218,33 @@ def test_torch_forecast_broker_allows_dynamic_entries_until_total_limit(make_set
assert len(broker.open_positions()) == 3
def test_torch_forecast_broker_respects_configured_symbol_position_limit(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
strategy_mode="torch_forecast",
min_position_usdt=1,
max_position_usdt=20,
max_symbol_exposure_usdt=20,
max_total_exposure_usdt=80,
max_open_positions=20,
max_positions_per_symbol=2,
max_entries_per_minute=0,
)
storage = Storage(settings.database_path)
broker = PaperBroker(settings, storage)
ticker = Ticker("BTCUSDT", 100, 99.9, 100.1, 10_000_000, 100, 0)
instrument = Instrument("BTCUSDT", "BTC", "USDT", "Trading", 0.01, 0.000001, 0.000001, 1)
first = broker.buy(Signal("BTCUSDT", "BUY", 0.8, "first", {"position_notional_usdt": 2}), ticker, instrument, {"BTCUSDT": 100})
second = broker.buy(Signal("BTCUSDT", "BUY", 0.8, "second", {"position_notional_usdt": 2}), ticker, instrument, {"BTCUSDT": 100})
third = broker.buy(Signal("BTCUSDT", "BUY", 0.8, "third", {"position_notional_usdt": 2}), ticker, instrument, {"BTCUSDT": 100})
assert first is not None
assert second is not None
assert third is None
assert len(broker.open_positions()) == 2
def test_trend_macd_closes_old_paper_positions_outside_symbol_universe(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
tests/test_strategy.py
+80 -15
View File
@@ -263,7 +263,8 @@ def test_torch_forecast_buys_only_from_positive_torch_edge(make_settings, tmp_pa
assert signal.action == "BUY"
assert signal.diagnostics["strategy_mode"] == "torch_forecast"
assert signal.diagnostics["checks"]["torch_model_ok"] is True
assert signal.diagnostics["position_notional_usdt"] == 25.0
assert signal.diagnostics["position_sizing"]["method"] == "torch_forecast_fractional_kelly"
assert settings.min_position_usdt <= signal.diagnostics["position_notional_usdt"] <= settings.max_position_usdt
def test_torch_forecast_blocks_without_valid_torch_model(make_settings, tmp_path) -> None:
@@ -328,6 +329,71 @@ def test_torch_forecast_allows_additional_entries_until_symbol_limit(make_settin
assert "symbol position limit" in capped.reason
def test_torch_forecast_kelly_buys_only_remaining_symbol_allocation(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
strategy_mode="torch_forecast",
min_position_usdt=1,
max_position_usdt=8,
max_symbol_exposure_usdt=25,
max_positions_per_symbol=6,
stop_loss_percent=0.04,
take_profit_percent=0.035,
kelly_sizing_enabled=True,
kelly_fraction=0.25,
kelly_max_fraction=0.20,
time_series_min_edge_percent=0.10,
time_series_min_probability_up=0.47,
)
strategy = SpotStrategy(settings)
ticker = Ticker("BTCUSDT", 105, 104.99, 105.01, 10_000_000, 1000, 1.0)
forecast = {
"usable": True,
"model": "torch_gru",
"expected_return_percent": 0.60,
"probability_up": 0.84,
"skill": 0.22,
"block_entry": False,
}
first = strategy.entry_signal(
"BTCUSDT",
[],
ticker,
open_positions_for_symbol=0,
forecast=forecast,
account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 0.0},
)
second = strategy.entry_signal(
"BTCUSDT",
[],
ticker,
open_positions_for_symbol=1,
forecast=forecast,
account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 8.0},
)
filled = strategy.entry_signal(
"BTCUSDT",
[],
ticker,
open_positions_for_symbol=2,
forecast=forecast,
account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 20.0},
)
first_sizing = first.diagnostics["position_sizing"]
second_sizing = second.diagnostics["position_sizing"]
assert first.action == "BUY"
assert first_sizing["method"] == "torch_forecast_fractional_kelly"
assert first_sizing["kelly_target_notional_usdt"] > settings.max_position_usdt
assert first.diagnostics["position_notional_usdt"] == settings.max_position_usdt
assert second.action == "BUY"
assert 1 <= second.diagnostics["position_notional_usdt"] < settings.max_position_usdt
assert second_sizing["kelly_open_symbol_exposure_usdt"] == 8.0
assert filled.action == "HOLD"
assert filled.diagnostics["checks"]["risk_size_ok"] is False
def test_torch_forecast_blocks_failed_quality_gate(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
@@ -362,7 +428,7 @@ def test_torch_forecast_blocks_failed_quality_gate(make_settings, tmp_path) -> N
assert signal.diagnostics["checks"]["quality_gate_ok"] is False
def test_torch_forecast_probe_buys_on_positive_high_probability(make_settings, tmp_path) -> None:
def test_torch_forecast_probe_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
strategy_mode="torch_forecast",
@@ -395,11 +461,11 @@ def test_torch_forecast_probe_buys_on_positive_high_probability(make_settings, t
account={"equity": 100.0},
)
assert signal.action == "BUY"
assert signal.action == "HOLD"
assert signal.diagnostics["edge_mode"] == "probe"
assert signal.diagnostics["checks"]["expected_edge_ok"] is True
assert signal.diagnostics["position_sizing"]["edge_mode"] == "probe"
assert settings.min_position_usdt <= signal.diagnostics["position_notional_usdt"] < settings.max_position_usdt
assert signal.diagnostics["checks"]["risk_size_ok"] is False
assert signal.diagnostics["position_notional_usdt"] == 0.0
def test_torch_forecast_probe_blocks_negative_expected_return(make_settings, tmp_path) -> None:
@@ -436,7 +502,7 @@ def test_torch_forecast_probe_blocks_negative_expected_return(make_settings, tmp
assert signal.diagnostics["checks"]["expected_edge_ok"] is False
def test_torch_forecast_rebound_overlay_buys_stabilized_drop(make_settings, tmp_path) -> None:
def test_torch_forecast_rebound_overlay_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
strategy_mode="torch_forecast",
@@ -479,12 +545,12 @@ def test_torch_forecast_rebound_overlay_buys_stabilized_drop(make_settings, tmp_
account={"equity": 100.0},
)
assert signal.action == "BUY"
assert signal.diagnostics["entry_path"] == "rebound"
assert signal.action == "HOLD"
assert signal.diagnostics["rebound"]["active"] is True
assert signal.diagnostics["edge_mode"] == "rebound"
assert signal.diagnostics["model_rebound_entry_ok"] is True
assert signal.diagnostics["rebound_entry_sized_ok"] is False
assert signal.diagnostics["checks"]["expected_edge_ok"] is False
assert signal.diagnostics["position_notional_usdt"] <= settings.rebound_max_position_usdt
assert signal.diagnostics["checks"]["risk_size_ok"] is False
def test_torch_forecast_rebound_overlay_does_not_buy_negative_forecast(make_settings, tmp_path) -> None:
@@ -532,7 +598,7 @@ def test_torch_forecast_rebound_overlay_does_not_buy_negative_forecast(make_sett
assert signal.diagnostics["edge_mode"] == "blocked"
def test_torch_forecast_rebound_fallback_buys_when_symbol_has_no_model(make_settings, tmp_path) -> None:
def test_torch_forecast_rebound_fallback_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
settings = make_settings(
tmp_path,
strategy_mode="torch_forecast",
@@ -571,12 +637,11 @@ def test_torch_forecast_rebound_fallback_buys_when_symbol_has_no_model(make_sett
account={"equity": 100.0},
)
assert signal.action == "BUY"
assert signal.diagnostics["entry_path"] == "rebound_fallback"
assert signal.action == "HOLD"
assert signal.diagnostics["fallback_rebound_entry_ok"] is True
assert signal.diagnostics["rebound_entry_sized_ok"] is False
assert signal.diagnostics["missing_torch_model"] is True
assert signal.diagnostics["edge_mode"] == "rebound_fallback"
assert signal.diagnostics["position_notional_usdt"] <= settings.rebound_max_position_usdt
assert signal.diagnostics["checks"]["risk_size_ok"] is False
def test_torch_forecast_rebound_fallback_can_be_disabled(make_settings, tmp_path) -> None:
tools/install_windows_torch_retrainer.ps1
+1 -1
View File
@@ -2,7 +2,7 @@
param(
[string]$TaskName = "TradeBot PyTorch Forecaster Retrainer",
[int]$EveryHours = 6,
[string]$Symbols = "BTCUSDT,ETHUSDT,SOLUSDT,LTCUSDT",
[string]$Symbols = "",
[int]$Limit = 3000,
[int]$Horizon = 0,
[string]$Horizons = "",
tools/run_retrain_until_replay8.ps1
+4 -2
View File
@@ -2,7 +2,7 @@
param(
[int]$MinReplayTrades = 8,
[int]$MaxAttempts = 0,
[string]$Symbols = "BTCUSDT,ETHUSDT,SOLUSDT,LTCUSDT",
[string]$Symbols = "",
[int]$Limit = 3000,
[switch]$DeployToPi,
[string]$PiHost = "192.168.0.185",
@@ -119,10 +119,12 @@ while ($true) {
"-NoProfile",
"-ExecutionPolicy", "Bypass",
"-File", $Runner,
"-Symbols", $Symbols,
"-Limit", $Limit.ToString(),
"-Seed", $attemptSeed.ToString()
)
if ($Symbols) {
$runnerArgs += @("-Symbols", $Symbols)
}
if ($DeployToPi) {
$runnerArgs += "-DeployToPi"
if ($PiHost) { $runnerArgs += @("-PiHost", $PiHost) }