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8dec344 606e49b 8dec344 7708226 8dec344 7708226 8dec344 3c53771 8dec344 7708226 8dec344 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 | from __future__ import annotations
import math
import os
from statistics import mean
from typing import Any
from schemas import HealthResponse, PredictRequest, PredictResponse, PredictionItem
class ChronosService:
"""HF Space service wrapper for Chronos-2."""
def __init__(self) -> None:
self.model_id = "chronos"
self.model_name = os.getenv(
"CHRONOS_MODEL_NAME",
"amazon/chronos-2",
)
self.backend = os.getenv("CHRONOS_BACKEND", "hf_cpu").strip() or "hf_cpu"
self.device = "cpu"
self.max_context_length = int(os.getenv("CHRONOS_MAX_CONTEXT_LENGTH", "512"))
self.max_horizon_step = int(os.getenv("CHRONOS_MAX_HORIZON_STEP", "288"))
self.confidence_floor = float(os.getenv("CHRONOS_CONFIDENCE_FLOOR", "0.16"))
self.confidence_ceiling = float(os.getenv("CHRONOS_CONFIDENCE_CEILING", "0.80"))
self.min_required_points = int(os.getenv("CHRONOS_MIN_REQUIRED_POINTS", "32"))
self.num_samples = int(os.getenv("CHRONOS_NUM_SAMPLES", "20"))
self.allow_baseline_fallback = os.getenv("CHRONOS_ALLOW_BASELINE_FALLBACK", "false").lower() == "true"
self.ready = False
self.load_error = ""
self._torch = None
self._pipeline = None
self._initialize_backend()
def health(self) -> HealthResponse:
return HealthResponse(
status="ok" if self.ready else "degraded",
model=self.model_name,
model_id=self.model_id,
backend=self.backend,
device=self.device,
ready=self.ready,
max_context_length=self.max_context_length,
max_horizon_step=self.max_horizon_step,
)
def predict(self, payload: PredictRequest) -> PredictResponse:
self._validate_request(payload)
closes = payload.close_prices[-payload.context_length :]
if self.backend == "hf_cpu":
if not self.ready:
raise RuntimeError(self.load_error or "chronos backend not ready")
predictions = self._predict_with_hf(closes, payload.horizons)
else:
predictions = self._predict_with_baseline(closes, payload.horizons)
return PredictResponse(model_id=self.model_id, predictions=predictions)
def _initialize_backend(self) -> None:
if self.backend == "baseline_cpu":
self.ready = True
return
if self.backend != "hf_cpu":
raise ValueError(f"unsupported CHRONOS_BACKEND={self.backend}")
try:
self._load_hf_model()
self.ready = True
except Exception as exc:
self.load_error = f"chronos hf load failed: {exc}"
if self.allow_baseline_fallback:
self.backend = "baseline_cpu"
self.ready = True
else:
self.ready = False
def _load_hf_model(self) -> None:
import torch
from chronos import Chronos2Pipeline
self._torch = torch
torch.set_num_threads(max(1, int(os.getenv("CHRONOS_TORCH_THREADS", "2"))))
self._pipeline = Chronos2Pipeline.from_pretrained(
self.model_name,
device_map="cpu",
)
def _predict_with_hf(
self, close_prices: list[float], horizons: list[int]
) -> list[PredictionItem]:
assert self._torch is not None
assert self._pipeline is not None
torch = self._torch
# Chronos-2 expects (n_series, n_variates, history_length) for tensor input.
context = torch.tensor(
close_prices[-self.max_context_length :],
dtype=torch.float32,
).reshape(1, 1, -1)
forecast = self._pipeline.predict(
context,
prediction_length=self.max_horizon_step,
)
dense_mean, dense_conf = self._extract_forecast(forecast)
if len(dense_mean) < max(horizons):
raise RuntimeError(
f"Chronos output horizon {len(dense_mean)} is shorter than requested {max(horizons)}"
)
predictions: list[PredictionItem] = []
for step in horizons:
predictions.append(
PredictionItem(
step=step,
pred_price=round(max(0.00000001, float(dense_mean[step - 1])), 8),
pred_confidence=round(dense_conf[step - 1], 4),
)
)
return predictions
def _extract_forecast(self, forecast: Any) -> tuple[list[float], list[float]]:
assert self._torch is not None
assert self._pipeline is not None
torch = self._torch
if isinstance(forecast, (list, tuple)):
if not forecast:
raise RuntimeError("empty Chronos forecast output")
if len(forecast) != 1:
raise RuntimeError(f"unexpected Chronos batch size: {len(forecast)}")
forecast = forecast[0]
if hasattr(forecast, "detach"):
tensor = forecast.detach().cpu()
else:
tensor = torch.as_tensor(forecast)
tensor = tensor.to(dtype=torch.float32)
squeezed = tensor.squeeze()
if squeezed.ndim == 0:
raise RuntimeError(f"unexpected Chronos forecast shape: {tuple(tensor.shape)}")
if squeezed.ndim == 1:
mean_forecast = squeezed.tolist()
std_forecast = [0.0 for _ in mean_forecast]
else:
if squeezed.ndim == 2:
quantile_tensor = squeezed
else:
quantile_tensor = squeezed.reshape(-1, squeezed.shape[-2], squeezed.shape[-1]).mean(dim=0)
quantiles = list(getattr(self._pipeline, "quantiles", []))
if not quantiles:
median_idx = quantile_tensor.shape[0] // 2
mean_forecast = quantile_tensor[median_idx].tolist()
std_forecast = [0.0 for _ in mean_forecast]
else:
median_idx = min(range(len(quantiles)), key=lambda idx: abs(quantiles[idx] - 0.5))
lower_idx = min(range(len(quantiles)), key=lambda idx: abs(quantiles[idx] - 0.1))
upper_idx = min(range(len(quantiles)), key=lambda idx: abs(quantiles[idx] - 0.9))
mean_forecast = quantile_tensor[median_idx].tolist()
lower_forecast = quantile_tensor[lower_idx].tolist()
upper_forecast = quantile_tensor[upper_idx].tolist()
std_forecast = [
max(0.0, (float(upper) - float(lower)) / 2.0)
for lower, upper in zip(lower_forecast, upper_forecast)
]
confidence: list[float] = []
for pred, std in zip(mean_forecast, std_forecast):
dispersion = abs(float(std)) / max(abs(float(pred)), 1e-6)
raw = 1.0 / (1.0 + dispersion)
confidence.append(max(self.confidence_floor, min(self.confidence_ceiling, raw)))
return [float(item) for item in mean_forecast], confidence
def _validate_request(self, payload: PredictRequest) -> None:
if payload.context_length > self.max_context_length:
raise ValueError(
f"context_length {payload.context_length} exceeds "
f"CHRONOS_MAX_CONTEXT_LENGTH={self.max_context_length}"
)
if payload.context_length > len(payload.close_prices):
raise ValueError("context_length must not exceed len(close_prices)")
if len(payload.close_prices) < self.min_required_points:
raise ValueError(
f"at least {self.min_required_points} close prices are required "
"for Chronos stability"
)
if any(step > self.max_horizon_step for step in payload.horizons):
raise ValueError(
f"horizons contain values above CHRONOS_MAX_HORIZON_STEP={self.max_horizon_step}"
)
def _predict_with_baseline(
self, close_prices: list[float], horizons: list[int]
) -> list[PredictionItem]:
last_price = close_prices[-1]
short_window = close_prices[-min(10, len(close_prices)) :]
mid_window = close_prices[-min(24, len(close_prices)) :]
long_window = close_prices[-min(64, len(close_prices)) :]
short_mean = mean(short_window)
mid_mean = mean(mid_window)
long_mean = mean(long_window)
momentum = 0.0 if short_mean == 0 else (last_price - short_mean) / short_mean
mean_reversion = 0.0 if long_mean == 0 else (mid_mean - long_mean) / long_mean
local_trend = self._slope(mid_window)
predictions: list[PredictionItem] = []
for step in horizons:
horizon_scale = min(1.0, math.log(step + 1.0) / 3.8)
expected_return = momentum * 0.35 + mean_reversion * 0.30 + local_trend * 0.35
expected_return *= horizon_scale
pred_price = max(0.00000001, last_price * (1.0 + expected_return))
confidence = self._baseline_confidence(close_prices, step, abs(expected_return))
predictions.append(
PredictionItem(
step=step,
pred_price=round(pred_price, 8),
pred_confidence=round(confidence, 4),
)
)
return predictions
def _baseline_confidence(
self, close_prices: list[float], step: int, expected_move_abs: float
) -> float:
if len(close_prices) < 3:
return self.confidence_floor
changes: list[float] = []
for previous, current in zip(close_prices[:-1], close_prices[1:]):
if previous <= 0:
continue
changes.append(abs((current - previous) / previous))
realized_vol = mean(changes[-min(64, len(changes)) :]) if changes else 0.0
stability = max(0.0, 1.0 - min(realized_vol * 18.0, 1.0))
horizon_decay = 1.0 / (1.0 + math.log(step + 1.0))
raw = 0.20 + min(expected_move_abs / (realized_vol + 1e-9), 2.0) * 0.18
raw += stability * 0.20 + horizon_decay * 0.22
return max(self.confidence_floor, min(self.confidence_ceiling, raw))
@staticmethod
def _slope(values: list[float]) -> float:
if len(values) < 2 or values[0] == 0:
return 0.0
return (values[-1] - values[0]) / values[0]
def describe_runtime(self) -> dict[str, Any]:
return {
"model_id": self.model_id,
"model_name": self.model_name,
"backend": self.backend,
"device": self.device,
"ready": self.ready,
"load_error": self.load_error,
"max_context_length": self.max_context_length,
"max_horizon_step": self.max_horizon_step,
"min_required_points": self.min_required_points,
}
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