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.venv/
__pycache__/
*.py[cod]
.pytest_cache/
data/raw/
data/manifest.json
artifacts/
artifacts_*/
reports/
predictions/
data_*/

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# cbpred
这是一个统一模型的 8H U 本位 USDT 合约回调预测管线。它只使用 banbot 的 K 线 OHLCV 数据，默认调用 banbot 下载 1H 存储数据，再按完整的 8 根 1H K 线聚合为 8H；不使用资金费率、盘口、持仓量或外部行情。
目标定义为有符号的未来最低价收益，并按品种训练期波动尺度归一化：
```text
raw_return = quantile(low[t+1:t+6], 0.20) / anchor_close - 1
target = raw_return / asset_atr_price_top20_median
```
这里的 20% 分位数是未来窗口的稳健低点参考，不会被单根异常影线完全决定；它不是严格的 `lowestLow`。负值表示该低点参考低于当前收盘价，正值表示它仍高于当前收盘价；因此上涨阶段不会被强行压成 0。`asset_atr_price_top20_median` 是该品种训练期 ATR/price 降序前 20% 的中位数，预测输出会乘回该尺度。训练锚点和未来 6 根必须连续，跨越测试边界的训练样本会被丢弃，避免把测试期标签泄漏进训练集。
## 运行
先安装依赖：
```bash
python3 -m venv .venv
.venv/bin/pip install -e .
```
使用 banbot 的配置连接数据库，筛选 `market=linear`、符号为 `BASE/USDT:USDT`、上市至少 730 天且未退市的品种；默认连接配置来自 `/home/ctyun/ban/banbot/biz/config.yml`：
```bash
.venv/bin/python -m cbpred prepare \
--banbot-config /home/ctyun/ban/banbot/biz/config.yml \
--out-dir data
```
如果数据已经由 banbot 下载完成，可以跳过下载，只执行筛选和导出：
```bash
.venv/bin/python -m cbpred prepare --skip-download --out-dir data
```
训练会执行三轮特征迭代。点模型候选包括随机森林平方误差和 ExtraTrees，另用 HistGradientBoosting 分位数模型输出 q20/q80；验证集和测试集均为最近 10 个月中的按品种随机拆分，测试集只在最终选模后使用：
```bash
.venv/bin/python -m cbpred train \
--data-dir data \
--artifact-dir artifacts_signed
```
默认保留所有连续样本，把当前 ATR 在过去 200 根 8H K 线中的排名作为特征；可以通过 `--volatility-rank-threshold 0.80` 或 `0.90` 做高波动样本对照实验。训练阶段会把数据集行数、过滤前后数量、阶段和候选模型进度输出到 stderr。
一条命令完成准备和训练：
```bash
.venv/bin/python -m cbpred run --out-dir data --artifact-dir artifacts_signed
```
输出在 `artifacts_signed/report.md`、`artifacts_signed/report.json` 和 `artifacts_signed/model.joblib`。报告会列出三轮特征的验证表现、最终模型和测试集 MAE/RMSE、q80 覆盖率、q20 下界未跌破率。
对某个已导出的 8H CSV 预测任意锚点：
```bash
.venv/bin/python -m cbpred predict \
--artifact artifacts_signed/model.joblib \
--csv data/raw/123_BTC_USDT_USDT.csv \
--at 2026010100000000
```
`future_low_return_q20` 是稳健未来低点收益的较低分位，对应 `future_low_price_q20` 这个保守的价格下界；`future_low_return_q80` 是较浅、较乐观的分位。负收益才表示回撤，正收益表示稳健未来低点仍高于当前收盘价。兼容字段 `drawdown_*` 等于对应收益取负，实际覆盖率应以测试集校准报告为准，不能把 80% 当成保证概率。
如果预测时 `eligible_for_model` 为 `false`，说明过去 200 根不足以计算 ATR 排名，或显式设置的 ATR 排名门槛未达到，返回的模型数值为 `null`，应跳过该时刻。
## 特征迭代
第一轮使用 18 根 K 线的方向、实体/影线占比、收盘在 K 线中的位置、相对波幅、对数收益，以及基于法币成交额的 18 根窗口成交量排名和相对中位数；再加入两个固定的品种画像值：训练期 `ATR/price` 降序前 20% 的中位数、训练期法币成交额对数前 20% 的中位数。所有绝对价格都被转换为比例或滚动相对量，品种画像再用训练品种的均值/标准差标准化。
第二轮追加归一化 ATR、RSI、布林带位置/宽度、EMA 距离、趋势收益、滚动波动率和滚动回撤；第三轮追加 18 根窗口的趋势、波幅、方向比例、成交量趋势、收盘位置和指标交互摘要。三轮使用同一批训练/验证/测试边界，选择验证 q80 pinball loss 加覆盖率偏差后的最佳组合，最后只在测试集报告一次。
运行标准库回归测试：
```bash
.venv/bin/python -m unittest discover -v
```

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Metadata-Version: 2.4
Name: cbpred
Version: 0.1.0
Summary: Unified 8H crypto futures drawdown prediction pipeline
Requires-Python: >=3.10
Requires-Dist: joblib>=1.3
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Requires-Dist: psycopg[binary]>=3.1
Requires-Dist: PyYAML>=6.0
Requires-Dist: scikit-learn>=1.3

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README.md
pyproject.toml
cbpred/__init__.py
cbpred/__main__.py
cbpred/pipeline.py
cbpred.egg-info/PKG-INFO
cbpred.egg-info/SOURCES.txt
cbpred.egg-info/dependency_links.txt
cbpred.egg-info/entry_points.txt
cbpred.egg-info/requires.txt
cbpred.egg-info/top_level.txt
tests/test_pipeline.py

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[console_scripts]
cbpred = cbpred.__main__:main

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joblib>=1.3
numpy>=1.24
pandas>=2.0
psycopg[binary]>=3.1
PyYAML>=6.0
scikit-learn>=1.3

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cbpred

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"""Unified 8H drawdown prediction pipeline."""
__version__ = "0.1.0"

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from __future__ import annotations
import argparse
import json
from pathlib import Path
from .pipeline import (
VOLATILITY_RANK_THRESHOLD,
parse_time_ms,
predict_from_csv,
prepare_data,
run_training,
)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Train a unified 8H futures drawdown model")
sub = parser.add_subparsers(dest="command", required=True)
prepare = sub.add_parser("prepare", help="filter symbols, call banbot, export complete 8H CSVs")
prepare.add_argument("--out-dir", type=Path, default=Path("data"))
prepare.add_argument("--banbot-dir", type=Path, default=Path("/home/ctyun/ban/banbot"))
prepare.add_argument("--banbot-config", type=Path, default=Path("/home/ctyun/ban/banbot/biz/config.yml"))
prepare.add_argument("--dsn", default=None)
prepare.add_argument("--db-type", choices=["auto", "questdb", "timescale"], default="auto")
prepare.add_argument("--exchange", default="binance")
prepare.add_argument("--end", dest="end_ms", default=None, help="UTC date/ISO timestamp or milliseconds")
prepare.add_argument("--history-months", type=int, default=36)
prepare.add_argument("--holdout-months", type=int, default=10)
prepare.add_argument("--min-age-days", type=int, default=730)
prepare.add_argument("--chunk-size", type=int, default=100)
prepare.add_argument("--banbot-cmd", default="go run .")
prepare.add_argument("--banbot-export-cmd", default="go run ./cmd/cbpred-export")
prepare.add_argument("--skip-download", action="store_true")
train = sub.add_parser("train", help="build three feature stages, tune on validation symbols, test once")
train.add_argument("--data-dir", type=Path, default=Path("data"))
train.add_argument("--artifact-dir", type=Path, default=Path("artifacts_signed"))
train.add_argument("--end", dest="end_ms", default=None)
train.add_argument("--test-months", type=int, default=10)
train.add_argument("--seed", type=int, default=42)
train.add_argument("--n-estimators", type=int, default=250)
train.add_argument("--n-jobs", type=int, default=-1)
train.add_argument("--volatility-rank-threshold", type=float, default=VOLATILITY_RANK_THRESHOLD)
run = sub.add_parser("run", help="prepare data and train the model")
for action in prepare._actions[1:]:
if action.dest not in {"help", "command"}:
run._add_action(action)
run.add_argument("--artifact-dir", type=Path, default=Path("artifacts_signed"))
run.add_argument("--test-months", type=int, default=10)
run.add_argument("--seed", type=int, default=42)
run.add_argument("--n-estimators", type=int, default=250)
run.add_argument("--n-jobs", type=int, default=-1)
run.add_argument("--volatility-rank-threshold", type=float, default=VOLATILITY_RANK_THRESHOLD)
predict = sub.add_parser(
"predict", help="predict signed robust future-low q20/q80 from an exported 8H CSV"
)
predict.add_argument("--artifact", type=Path, default=Path("artifacts_signed/model.joblib"))
predict.add_argument("--csv", type=Path, required=True)
predict.add_argument("--at", dest="at_ms", default=None)
predict.add_argument("--symbol", default=None, help="symbol used for the fixed training asset profile")
return parser
def main() -> None:
args = build_parser().parse_args()
if args.command == "prepare":
result = prepare_data(
out_dir=args.out_dir,
banbot_dir=args.banbot_dir,
banbot_config=args.banbot_config,
dsn=args.dsn,
db_type=args.db_type,
exchange=args.exchange,
end_ms=parse_time_ms(args.end_ms) if args.end_ms else None,
history_months=args.history_months,
holdout_months=args.holdout_months,
min_age_days=args.min_age_days,
chunk_size=args.chunk_size,
skip_download=args.skip_download,
banbot_cmd=args.banbot_cmd,
banbot_export_cmd=args.banbot_export_cmd,
)
print(json.dumps({"selected": len(result["symbols"]), "excluded": len(result["excluded"])}, indent=2))
elif args.command == "train":
result = run_training(
data_dir=args.data_dir,
artifact_dir=args.artifact_dir,
end_ms=parse_time_ms(args.end_ms) if args.end_ms else None,
test_months=args.test_months,
seed=args.seed,
n_estimators=args.n_estimators,
n_jobs=args.n_jobs,
volatility_rank_threshold=args.volatility_rank_threshold,
)
print(json.dumps(result, indent=2, ensure_ascii=False))
elif args.command == "run":
prepare_data(
out_dir=args.out_dir,
banbot_dir=args.banbot_dir,
banbot_config=args.banbot_config,
dsn=args.dsn,
db_type=args.db_type,
exchange=args.exchange,
end_ms=parse_time_ms(args.end_ms) if args.end_ms else None,
history_months=args.history_months,
holdout_months=args.holdout_months,
min_age_days=args.min_age_days,
chunk_size=args.chunk_size,
skip_download=args.skip_download,
banbot_cmd=args.banbot_cmd,
banbot_export_cmd=args.banbot_export_cmd,
)
result = run_training(
data_dir=args.out_dir,
artifact_dir=args.artifact_dir,
end_ms=parse_time_ms(args.end_ms) if args.end_ms else None,
test_months=args.test_months,
seed=args.seed,
n_estimators=args.n_estimators,
n_jobs=args.n_jobs,
volatility_rank_threshold=args.volatility_rank_threshold,
)
print(json.dumps(result, indent=2, ensure_ascii=False))
else:
result = predict_from_csv(
artifact_path=args.artifact,
csv_path=args.csv,
at_ms=parse_time_ms(args.at_ms) if args.at_ms else None,
symbol=args.symbol,
)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()

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package main
import (
"encoding/csv"
"flag"
"fmt"
"os"
"path/filepath"
"strconv"
"strings"
"github.com/banbox/banbot/biz"
"github.com/banbox/banbot/config"
"github.com/banbox/banbot/core"
"github.com/banbox/banbot/orm"
)
var columns = []string{
"timestamp", "open", "high", "low", "close", "volume", "quote", "buy_volume", "trade_num",
}
func main() {
configPath := flag.String("config", "", "banbot config path")
dataDir := flag.String("data-dir", "", "banbot data directory")
pairs := flag.String("pairs", "", "comma-separated symbols")
startMS := flag.Int64("start-ms", 0, "inclusive start timestamp in milliseconds")
endMS := flag.Int64("end-ms", 0, "exclusive end timestamp in milliseconds")
outDir := flag.String("out-dir", "", "CSV output directory")
flag.Parse()
if *configPath == "" || *dataDir == "" || *pairs == "" || *outDir == "" || *startMS <= 0 || *endMS <= *startMS {
flag.Usage()
os.Exit(2)
}
args := &config.CmdArgs{
Configs: config.ArrString{*configPath},
DataDir: *dataDir,
NoDefault: true,
RawPairs: *pairs,
TimeStart: strconv.FormatInt(*startMS, 10),
TimeEnd: strconv.FormatInt(*endMS, 10),
LogLevel: "error",
}
if err := biz.SetupComs(args); err != nil {
fail(err)
}
defer core.RunExitCalls()
if err := os.MkdirAll(*outDir, 0o755); err != nil {
fail(err)
}
for _, rawPair := range strings.Split(*pairs, ",") {
pair := strings.TrimSpace(rawPair)
if pair == "" {
continue
}
exs, err := orm.GetExSymbolCur(pair)
if err != nil {
fail(err)
}
_, rows, err := orm.GetSeries(exs, "8h", *startMS, *endMS, 0, false)
if err != nil {
fail(err)
}
name := fmt.Sprintf("%d_%s.csv", exs.ID, strings.NewReplacer("/", "_", ":", "_").Replace(pair))
if err := writeCSV(filepath.Join(*outDir, name), rows, exs); err != nil {
fail(err)
}
}
}
func writeCSV(path string, rows []*orm.DataSeries, exs *orm.ExSymbol) error {
file, err := os.Create(path)
if err != nil {
return err
}
defer file.Close()
writer := csv.NewWriter(file)
if err := writer.Write(columns); err != nil {
return err
}
for _, row := range rows {
bar, err := row.OHLCV(exs)
if err != nil {
return err
}
values := []string{
strconv.FormatInt(bar.Time, 10),
strconv.FormatFloat(bar.Open, 'f', -1, 64),
strconv.FormatFloat(bar.High, 'f', -1, 64),
strconv.FormatFloat(bar.Low, 'f', -1, 64),
strconv.FormatFloat(bar.Close, 'f', -1, 64),
strconv.FormatFloat(bar.Volume, 'f', -1, 64),
strconv.FormatFloat(bar.Quote, 'f', -1, 64),
strconv.FormatFloat(bar.BuyVolume, 'f', -1, 64),
strconv.FormatInt(bar.TradeNum, 10),
}
if err := writer.Write(values); err != nil {
return err
}
}
writer.Flush()
return writer.Error()
}
func fail(err error) {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}

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我现在有很多品种的K线数据，需要你帮我训练一个机器学习模型，基于8H级别的K线
提取特征，输入最近18个K线（提取bar特征，比如阴线阳线，实体占比，实体的中心位置），
预测未来6个K线的最大回调幅度（相比预测时最新收盘价），有80%概率命中的位置最佳，或者最大回调幅度的大概80%位置。
应该用统一的模型预测所有品种，每个品种的还要提取一些特异信息，比如：
1. 最近18个K线的成交量排名（统一基于法币）
2. 训练数据中所有ATR/价格，降序，前20%的中位数
保留最新10个月的数据作为测试数据，使用此前的数据作为训练数据
核心目标是只使用K线数据，提取特征，结合品种独有特点，训练统一的模型，预测给定品种在任意时刻后续的回调幅度。
特征的选择上面只是一些示例，你应该根据K线特点，考虑还有哪些特征可以构造，哪些比较重要
比如或许可以结合一些指标，比如布林带之类的，但是注意使用指标提取特征时务必注意要进行归一化，确保不同品种的特征可比。
训练数据的某些特征是可以事先提取的，比如上面说到品种的所有训练数据的ATR/价格中位数，这样的特征应该是属于品种独有的特征，每个特征只提取一两个值表示即可，不应该采样过于复杂的数据避免过拟合。
然后在训练模型时，优先使用随机森林算法，也可以对比其他模型算法；
测试数据按品种随机划分为两部分，一部分作为验证集，一部分作为测试集。
我们的K线数据是通过 /home/ctyun/ban/banbot 项目下载维护到postgresql中的
应该只关注U本位合约中的USDT的品种，且上市至少2年；
先通过banbot获取所有符合的品种，下载它的8H的K线确保数据库中完整。
然后基于品种导出数据，方便python进行处理分析。
然后考虑如何构造特征，如何组织和处理数据作为训练友好的数据格式
然后创建多种机器学习模型，进行模型训练，使用验证集进行验证调优
最后统一使用测试集测试，给出最佳的模型
然后使用最佳模型，再次不断反思特征如何优化，考虑banbot支持的指标，重新处理数据重新训练
这个特征重新处理并训练的过程要重复3次；
最后给我输出最佳特征提取方法，以及模型的测试结果。
模型预测的未来6个K线的最大回调幅度，应该使用未来6个K线的lowestLow，这里可以尝试绝对误差和平方误差，或者其他你认为有必要常识的误差计算方法。
首先是，模型训练时必须输出进度，不能一直无限等待；
另外，其实大部分品种，它只是在少部分波动很大的时候才会有大幅回撤，这时候模型的输出才有意义，但波动大的只占K线的或许20%或者10%以内；训练数据中应该剔除波动太小的时候，这时候没有意义，不需要关注；我想到的一个方法是，可以用ATR在过去200个K线中的排名来度量波动大小，排名不够前的直接忽略，你实际找十来个品种的数据检测观察下，看看取前10%还是20%或者其他比例；然后对所有的训练数据处理后再继续。
实际上不同品种的回撤幅度差异很大，而且和他们所处的阶段有关系；在刚开始上升的时候，多半未来6个k线的最低点依然高于当前的收盘价，但这时候也不应该直接输出0，而是应该根据过往该品种的特征，输出一个符合当下的大概可能的回撤，这个应该作为真实值，这样计算的损失和误差就不会特别大，导致过低估计回撤了；或许你也可以考虑其他方法。总之不应该完全用未来6个k线的最低值作为回撤，而是应该用更有意义的值

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module cbpred-export
go 1.24.0
require github.com/banbox/banbot v0.0.0
require (
github.com/bahlo/generic-list-go v0.2.0 // indirect
github.com/banbox/banexg v0.2.60 // indirect
github.com/banbox/banta v0.3.7 // indirect
github.com/banbox/bntp v0.1.0 // indirect
github.com/banbox/cron/v3 v3.1.2 // indirect
github.com/beevik/ntp v1.4.3 // indirect
github.com/buger/jsonparser v1.1.1 // indirect
github.com/bytedance/gopkg v0.1.3 // indirect
github.com/bytedance/sonic v1.15.0 // indirect
github.com/bytedance/sonic/loader v0.5.0 // indirect
github.com/cespare/xxhash/v2 v2.3.0 // indirect
github.com/cloudwego/base64x v0.1.6 // indirect
github.com/cloudwego/eino v0.6.0 // indirect
github.com/cloudwego/eino-ext/components/model/openai v0.1.1 // indirect
github.com/cloudwego/eino-ext/libs/acl/openai v0.0.0-20250918130948-16e3a249e721 // indirect
github.com/dgraph-io/ristretto v0.2.0 // indirect
github.com/dustin/go-humanize v1.0.1 // indirect
github.com/ebitengine/purego v0.9.0 // indirect
github.com/eino-contrib/jsonschema v1.0.2 // indirect
github.com/evanphx/json-patch v0.5.2 // indirect
github.com/felixge/fgprof v0.9.5 // indirect
github.com/flopp/go-findfont v0.1.0 // indirect
github.com/fogleman/gg v1.3.0 // indirect
github.com/getkin/kin-openapi v0.118.0 // indirect
github.com/go-ole/go-ole v1.3.0 // indirect
github.com/go-openapi/jsonpointer v0.19.5 // indirect
github.com/go-openapi/swag v0.19.5 // indirect
github.com/go-telegram/bot v1.17.0 // indirect
github.com/go-viper/mapstructure/v2 v2.4.0 // indirect
github.com/golang/freetype v0.0.0-20170609003504-e2365dfdc4a0 // indirect
github.com/google/pprof v0.0.0-20250317173921-a4b03ec1a45e // indirect
github.com/google/uuid v1.6.0 // indirect
github.com/goph/emperror v0.17.2 // indirect
github.com/gorilla/websocket v1.5.3 // indirect
github.com/inconshreveable/mousetrap v1.1.0 // indirect
github.com/invopop/yaml v0.1.0 // indirect
github.com/jackc/pgpassfile v1.0.0 // indirect
github.com/jackc/pgservicefile v0.0.0-20240606120523-5a60cdf6a761 // indirect
github.com/jackc/pgx/v5 v5.7.6 // indirect
github.com/jackc/puddle/v2 v2.2.2 // indirect
github.com/josharian/intern v1.0.0 // indirect
github.com/json-iterator/go v1.1.12 // indirect
github.com/klauspost/cpuid/v2 v2.3.0 // indirect
github.com/lufia/plan9stats v0.0.0-20240909124753-873cd0166683 // indirect
github.com/mailru/easyjson v0.7.7 // indirect
github.com/mattn/go-isatty v0.0.20 // indirect
github.com/meguminnnnnnnnn/go-openai v0.0.0-20250821095446-07791bea23a0 // indirect
github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db // indirect
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
github.com/modern-go/reflect2 v1.0.2 // indirect
github.com/mohae/deepcopy v0.0.0-20170929034955-c48cc78d4826 // indirect
github.com/ncruces/go-strftime v0.1.9 // indirect
github.com/nikolalohinski/gonja v1.5.3 // indirect
github.com/pelletier/go-toml/v2 v2.0.9 // indirect
github.com/perimeterx/marshmallow v1.1.4 // indirect
github.com/petermattis/goid v0.0.0-20250813065127-a731cc31b4fe // indirect
github.com/pkg/errors v0.9.1 // indirect
github.com/power-devops/perfstat v0.0.0-20240221224432-82ca36839d55 // indirect
github.com/remyoudompheng/bigfft v0.0.0-20230129092748-24d4a6f8daec // indirect
github.com/richardlehane/mscfb v1.0.4 // indirect
github.com/richardlehane/msoleps v1.0.4 // indirect
github.com/rivo/uniseg v0.4.7 // indirect
github.com/sasha-s/go-deadlock v0.3.6 // indirect
github.com/schollz/progressbar/v3 v3.18.0 // indirect
github.com/shirou/gopsutil/v4 v4.25.9 // indirect
github.com/shopspring/decimal v1.4.0 // indirect
github.com/sirupsen/logrus v1.9.3 // indirect
github.com/slongfield/pyfmt v0.0.0-20220222012616-ea85ff4c361f // indirect
github.com/spf13/cobra v1.10.2 // indirect
github.com/spf13/pflag v1.0.9 // indirect
github.com/tiendc/go-deepcopy v1.7.1 // indirect
github.com/tklauser/go-sysconf v0.3.15 // indirect
github.com/tklauser/numcpus v0.10.0 // indirect
github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
github.com/wk8/go-ordered-map/v2 v2.1.8 // indirect
github.com/xuri/efp v0.0.1 // indirect
github.com/xuri/excelize/v2 v2.10.0 // indirect
github.com/xuri/nfp v0.0.2-0.20250530014748-2ddeb826f9a9 // indirect
github.com/yargevad/filepathx v1.0.0 // indirect
github.com/yusufpapurcu/wmi v1.2.4 // indirect
go.uber.org/multierr v1.11.0 // indirect
go.uber.org/zap v1.27.0 // indirect
golang.org/x/arch v0.22.0 // indirect
golang.org/x/crypto v0.43.0 // indirect
golang.org/x/exp v0.0.0-20250718183923-645b1fa84792 // indirect
golang.org/x/image v0.32.0 // indirect
golang.org/x/net v0.46.0 // indirect
golang.org/x/sync v0.17.0 // indirect
golang.org/x/sys v0.37.0 // indirect
golang.org/x/term v0.36.0 // indirect
golang.org/x/text v0.30.0 // indirect
gonum.org/v1/gonum v0.16.0 // indirect
google.golang.org/genproto/googleapis/rpc v0.0.0-20250804133106-a7a43d27e69b // indirect
google.golang.org/grpc v1.76.0 // indirect
google.golang.org/protobuf v1.36.10 // indirect
gopkg.in/natefinch/lumberjack.v2 v2.2.1 // indirect
gopkg.in/yaml.v2 v2.4.0 // indirect
gopkg.in/yaml.v3 v3.0.1 // indirect
modernc.org/libc v1.66.10 // indirect
modernc.org/mathutil v1.7.1 // indirect
modernc.org/memory v1.11.0 // indirect
modernc.org/sqlite v1.39.1 // indirect
)
replace github.com/banbox/banbot => /home/ctyun/ban/banbot

379
go.sum Normal file
View File

@@ -0,0 +1,379 @@
github.com/airbrake/gobrake v3.6.1+incompatible/go.mod h1:wM4gu3Cn0W0K7GUuVWnlXZU11AGBXMILnrdOU8Kn00o=
github.com/bahlo/generic-list-go v0.2.0 h1:5sz/EEAK+ls5wF+NeqDpk5+iNdMDXrh3z3nPnH1Wvgk=
github.com/bahlo/generic-list-go v0.2.0/go.mod h1:2KvAjgMlE5NNynlg/5iLrrCCZ2+5xWbdbCW3pNTGyYg=
github.com/banbox/banexg v0.2.60 h1:0IQaSATKwf6SNLnehGmjdAidnYJsRqhJqruUfh9U8LM=
github.com/banbox/banexg v0.2.60/go.mod h1:T9JWRGblxAibQjwKK6VABG+r87y1LRnyYe7sz5aBnR4=
github.com/banbox/banta v0.3.7 h1:R1xVVtds0Temlc1fATX08uG7rR0pbukr3L6FikyKscE=
github.com/banbox/banta v0.3.7/go.mod h1:43XoXngLPip/pX1OvohnNUScScqb1/jtKxVqgltwZUU=
github.com/banbox/bntp v0.1.0 h1:kZROpbb3OME8XM8QeM+2SeQWQrHRDFN05okr8OITKAU=
github.com/banbox/bntp v0.1.0/go.mod h1:w0WP7GROKQo07wHFBm/FVgH4xlZuOYCoro0ky4iTC3M=
github.com/banbox/cron/v3 v3.1.2 h1:gBNnz/OBxIMU7iBCtObZKhb85rzbaXr8AZLZb40CgeM=
github.com/banbox/cron/v3 v3.1.2/go.mod h1:SZzzT03qsZ1y8/Pt2VqKb9Av/HfnuSargOQRmql7n18=
github.com/beevik/ntp v1.4.3 h1:PlbTvE5NNy4QHmA4Mg57n7mcFTmr1W1j3gcK7L1lqho=
github.com/beevik/ntp v1.4.3/go.mod h1:Unr8Zg+2dRn7d8bHFuehIMSvvUYssHMxW3Q5Nx4RW5Q=
github.com/bitly/go-simplejson v0.5.0/go.mod h1:cXHtHw4XUPsvGaxgjIAn8PhEWG9NfngEKAMDJEczWVA=
github.com/bmizerany/assert v0.0.0-20160611221934-b7ed37b82869/go.mod h1:Ekp36dRnpXw/yCqJaO+ZrUyxD+3VXMFFr56k5XYrpB4=
github.com/buger/jsonparser v1.1.1 h1:2PnMjfWD7wBILjqQbt530v576A/cAbQvEW9gGIpYMUs=
github.com/buger/jsonparser v1.1.1/go.mod h1:6RYKKt7H4d4+iWqouImQ9R2FZql3VbhNgx27UK13J/0=
github.com/bugsnag/bugsnag-go v1.4.0/go.mod h1:2oa8nejYd4cQ/b0hMIopN0lCRxU0bueqREvZLWFrtK8=
github.com/bugsnag/panicwrap v1.2.0/go.mod h1:D/8v3kj0zr8ZAKg1AQ6crr+5VwKN5eIywRkfhyM/+dE=
github.com/bytedance/gopkg v0.1.3 h1:TPBSwH8RsouGCBcMBktLt1AymVo2TVsBVCY4b6TnZ/M=
github.com/bytedance/gopkg v0.1.3/go.mod h1:576VvJ+eJgyCzdjS+c4+77QF3p7ubbtiKARP3TxducM=
github.com/bytedance/mockey v1.2.14 h1:KZaFgPdiUwW+jOWFieo3Lr7INM1P+6adO3hxZhDswY8=
github.com/bytedance/mockey v1.2.14/go.mod h1:1BPHF9sol5R1ud/+0VEHGQq/+i2lN+GTsr3O2Q9IENY=
github.com/bytedance/sonic v1.15.0 h1:/PXeWFaR5ElNcVE84U0dOHjiMHQOwNIx3K4ymzh/uSE=
github.com/bytedance/sonic v1.15.0/go.mod h1:tFkWrPz0/CUCLEF4ri4UkHekCIcdnkqXw9VduqpJh0k=
github.com/bytedance/sonic/loader v0.5.0 h1:gXH3KVnatgY7loH5/TkeVyXPfESoqSBSBEiDd5VjlgE=
github.com/bytedance/sonic/loader v0.5.0/go.mod h1:AR4NYCk5DdzZizZ5djGqQ92eEhCCcdf5x77udYiSJRo=
github.com/certifi/gocertifi v0.0.0-20190105021004-abcd57078448/go.mod h1:GJKEexRPVJrBSOjoqN5VNOIKJ5Q3RViH6eu3puDRwx4=
github.com/cespare/xxhash/v2 v2.3.0 h1:UL815xU9SqsFlibzuggzjXhog7bL6oX9BbNZnL2UFvs=
github.com/cespare/xxhash/v2 v2.3.0/go.mod h1:VGX0DQ3Q6kWi7AoAeZDth3/j3BFtOZR5XLFGgcrjCOs=
github.com/chengxilo/virtualterm v1.0.4 h1:Z6IpERbRVlfB8WkOmtbHiDbBANU7cimRIof7mk9/PwM=
github.com/chengxilo/virtualterm v1.0.4/go.mod h1:DyxxBZz/x1iqJjFxTFcr6/x+jSpqN0iwWCOK1q10rlY=
github.com/chromedp/cdproto v0.0.0-20230802225258-3cf4e6d46a89/go.mod h1:GKljq0VrfU4D5yc+2qA6OVr8pmO/MBbPEWqWQ/oqGEs=
github.com/chromedp/chromedp v0.9.2/go.mod h1:LkSXJKONWTCHAfQasKFUZI+mxqS4tZqhmtGzzhLsnLs=
github.com/chromedp/sysutil v1.0.0/go.mod h1:kgWmDdq8fTzXYcKIBqIYvRRTnYb9aNS9moAV0xufSww=
github.com/chzyer/logex v1.2.1/go.mod h1:JLbx6lG2kDbNRFnfkgvh4eRJRPX1QCoOIWomwysCBrQ=
github.com/chzyer/readline v1.5.1/go.mod h1:Eh+b79XXUwfKfcPLepksvw2tcLE/Ct21YObkaSkeBlk=
github.com/chzyer/test v1.0.0/go.mod h1:2JlltgoNkt4TW/z9V/IzDdFaMTM2JPIi26O1pF38GC8=
github.com/cloudwego/base64x v0.1.6 h1:t11wG9AECkCDk5fMSoxmufanudBtJ+/HemLstXDLI2M=
github.com/cloudwego/base64x v0.1.6/go.mod h1:OFcloc187FXDaYHvrNIjxSe8ncn0OOM8gEHfghB2IPU=
github.com/cloudwego/eino v0.6.0 h1:pobGKMOfcQHVNhD9UT/HrvO0eYG6FC2ML/NKY2Eb9+Q=
github.com/cloudwego/eino v0.6.0/go.mod h1:JNapfU+QUrFFpboNDrNOFvmz0m9wjBFHHCr77RH6a50=
github.com/cloudwego/eino-ext/components/model/openai v0.1.1 h1:VRdUDcnfi/T8F0jcuovhdADU9Io/oMqiKpY2ZJTBc1o=
github.com/cloudwego/eino-ext/components/model/openai v0.1.1/go.mod h1:VwAXEY1ik2K9KFPZvymnkfBQQKgLHbpg90yg+7hrTt8=
github.com/cloudwego/eino-ext/libs/acl/openai v0.0.0-20250918130948-16e3a249e721 h1:5Hd8GxNEmu+ppTGCRBU6kLKfCQNXPMwi31xA83PzEqo=
github.com/cloudwego/eino-ext/libs/acl/openai v0.0.0-20250918130948-16e3a249e721/go.mod h1:fHn/6OqPPY1iLLx9wzz+MEVT5Dl9gwuZte1oLEnCoYw=
github.com/cpuguy83/go-md2man/v2 v2.0.6/go.mod h1:oOW0eioCTA6cOiMLiUPZOpcVxMig6NIQQ7OS05n1F4g=
github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc h1:U9qPSI2PIWSS1VwoXQT9A3Wy9MM3WgvqSxFWenqJduM=
github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/dgraph-io/ristretto v0.2.0 h1:XAfl+7cmoUDWW/2Lx8TGZQjjxIQ2Ley9DSf52dru4WE=
github.com/dgraph-io/ristretto v0.2.0/go.mod h1:8uBHCU/PBV4Ag0CJrP47b9Ofby5dqWNh4FicAdoqFNU=
github.com/dgryski/go-farm v0.0.0-20200201041132-a6ae2369ad13 h1:fAjc9m62+UWV/WAFKLNi6ZS0675eEUC9y3AlwSbQu1Y=
github.com/dgryski/go-farm v0.0.0-20200201041132-a6ae2369ad13/go.mod h1:SqUrOPUnsFjfmXRMNPybcSiG0BgUW2AuFH8PAnS2iTw=
github.com/dustin/go-humanize v1.0.1 h1:GzkhY7T5VNhEkwH0PVJgjz+fX1rhBrR7pRT3mDkpeCY=
github.com/dustin/go-humanize v1.0.1/go.mod h1:Mu1zIs6XwVuF/gI1OepvI0qD18qycQx+mFykh5fBlto=
github.com/ebitengine/purego v0.9.0 h1:mh0zpKBIXDceC63hpvPuGLiJ8ZAa3DfrFTudmfi8A4k=
github.com/ebitengine/purego v0.9.0/go.mod h1:iIjxzd6CiRiOG0UyXP+V1+jWqUXVjPKLAI0mRfJZTmQ=
github.com/eino-contrib/jsonschema v1.0.2 h1:HaxruBMUdnXa7Lg/lX8g0Hk71ZIfdTZXmBQz0e3esr8=
github.com/eino-contrib/jsonschema v1.0.2/go.mod h1:cpnX4SyKjWjGC7iN2EbhxaTdLqGjCi0e9DxpLYxddD4=
github.com/evanphx/json-patch v0.5.2 h1:xVCHIVMUu1wtM/VkR9jVZ45N3FhZfYMMYGorLCR8P3k=
github.com/evanphx/json-patch v0.5.2/go.mod h1:ZWS5hhDbVDyob71nXKNL0+PWn6ToqBHMikGIFbs31qQ=
github.com/felixge/fgprof v0.9.5 h1:8+vR6yu2vvSKn08urWyEuxx75NWPEvybbkBirEpsbVY=
github.com/felixge/fgprof v0.9.5/go.mod h1:yKl+ERSa++RYOs32d8K6WEXCB4uXdLls4ZaZPpayhMM=
github.com/flopp/go-findfont v0.1.0 h1:lPn0BymDUtJo+ZkV01VS3661HL6F4qFlkhcJN55u6mU=
github.com/flopp/go-findfont v0.1.0/go.mod h1:wKKxRDjD024Rh7VMwoU90i6ikQRCr+JTHB5n4Ejkqvw=
github.com/fogleman/gg v1.3.0 h1:/7zJX8F6AaYQc57WQCyN9cAIz+4bCJGO9B+dyW29am8=
github.com/fogleman/gg v1.3.0/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
github.com/fsnotify/fsnotify v1.4.7/go.mod h1:jwhsz4b93w/PPRr/qN1Yymfu8t87LnFCMoQvtojpjFo=
github.com/getkin/kin-openapi v0.118.0 h1:z43njxPmJ7TaPpMSCQb7PN0dEYno4tyBPQcrFdHoLuM=
github.com/getkin/kin-openapi v0.118.0/go.mod h1:l5e9PaFUo9fyLJCPGQeXI2ML8c3P8BHOEV2VaAVf/pc=
github.com/getsentry/raven-go v0.2.0/go.mod h1:KungGk8q33+aIAZUIVWZDr2OfAEBsO49PX4NzFV5kcQ=
github.com/go-check/check v0.0.0-20180628173108-788fd7840127 h1:0gkP6mzaMqkmpcJYCFOLkIBwI7xFExG03bbkOkCvUPI=
github.com/go-check/check v0.0.0-20180628173108-788fd7840127/go.mod h1:9ES+weclKsC9YodN5RgxqK/VD9HM9JsCSh7rNhMZE98=
github.com/go-logr/logr v1.4.3 h1:CjnDlHq8ikf6E492q6eKboGOC0T8CDaOvkHCIg8idEI=
github.com/go-logr/logr v1.4.3/go.mod h1:9T104GzyrTigFIr8wt5mBrctHMim0Nb2HLGrmQ40KvY=
github.com/go-logr/stdr v1.2.2 h1:hSWxHoqTgW2S2qGc0LTAI563KZ5YKYRhT3MFKZMbjag=
github.com/go-logr/stdr v1.2.2/go.mod h1:mMo/vtBO5dYbehREoey6XUKy/eSumjCCveDpRre4VKE=
github.com/go-ole/go-ole v1.2.6/go.mod h1:pprOEPIfldk/42T2oK7lQ4v4JSDwmV0As9GaiUsvbm0=
github.com/go-ole/go-ole v1.3.0 h1:Dt6ye7+vXGIKZ7Xtk4s6/xVdGDQynvom7xCFEdWr6uE=
github.com/go-ole/go-ole v1.3.0/go.mod h1:5LS6F96DhAwUc7C+1HLexzMXY1xGRSryjyPPKW6zv78=
github.com/go-openapi/jsonpointer v0.19.5 h1:gZr+CIYByUqjcgeLXnQu2gHYQC9o73G2XUeOFYEICuY=
github.com/go-openapi/jsonpointer v0.19.5/go.mod h1:Pl9vOtqEWErmShwVjC8pYs9cog34VGT37dQOVbmoatg=
github.com/go-openapi/swag v0.19.5 h1:lTz6Ys4CmqqCQmZPBlbQENR1/GucA2bzYTE12Pw4tFY=
github.com/go-openapi/swag v0.19.5/go.mod h1:POnQmlKehdgb5mhVOsnJFsivZCEZ/vjK9gh66Z9tfKk=
github.com/go-telegram/bot v1.17.0 h1:Hs0kGxSj97QFqOQP0zxduY/4tSx8QDzvNI9uVRS+zmY=
github.com/go-telegram/bot v1.17.0/go.mod h1:i2TRs7fXWIeaceF3z7KzsMt/he0TwkVC680mvdTFYeM=
github.com/go-test/deep v1.0.8 h1:TDsG77qcSprGbC6vTN8OuXp5g+J+b5Pcguhf7Zt61VM=
github.com/go-test/deep v1.0.8/go.mod h1:5C2ZWiW0ErCdrYzpqxLbTX7MG14M9iiw8DgHncVwcsE=
github.com/go-viper/mapstructure/v2 v2.4.0 h1:EBsztssimR/CONLSZZ04E8qAkxNYq4Qp9LvH92wZUgs=
github.com/go-viper/mapstructure/v2 v2.4.0/go.mod h1:oJDH3BJKyqBA2TXFhDsKDGDTlndYOZ6rGS0BRZIxGhM=
github.com/gobwas/httphead v0.1.0/go.mod h1:O/RXo79gxV8G+RqlR/otEwx4Q36zl9rqC5u12GKvMCM=
github.com/gobwas/pool v0.2.1/go.mod h1:q8bcK0KcYlCgd9e7WYLm9LpyS+YeLd8JVDW6WezmKEw=
github.com/gobwas/ws v1.2.1/go.mod h1:hRKAFb8wOxFROYNsT1bqfWnhX+b5MFeJM9r2ZSwg/KY=
github.com/gofrs/uuid v3.2.0+incompatible/go.mod h1:b2aQJv3Z4Fp6yNu3cdSllBxTCLRxnplIgP/c0N/04lM=
github.com/golang/freetype v0.0.0-20170609003504-e2365dfdc4a0 h1:DACJavvAHhabrF08vX0COfcOBJRhZ8lUbR+ZWIs0Y5g=
github.com/golang/freetype v0.0.0-20170609003504-e2365dfdc4a0/go.mod h1:E/TSTwGwJL78qG/PmXZO1EjYhfJinVAhrmmHX6Z8B9k=
github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
github.com/golang/protobuf v1.5.4 h1:i7eJL8qZTpSEXOPTxNKhASYpMn+8e5Q6AdndVa1dWek=
github.com/golang/protobuf v1.5.4/go.mod h1:lnTiLA8Wa4RWRcIUkrtSVa5nRhsEGBg48fD6rSs7xps=
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pyproject.toml Normal file
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[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[project]
name = "cbpred"
version = "0.1.0"
description = "Unified 8H crypto futures drawdown prediction pipeline"
requires-python = ">=3.10"
dependencies = [
"joblib>=1.3",
"numpy>=1.24",
"pandas>=2.0",
"psycopg[binary]>=3.1",
"PyYAML>=6.0",
"scikit-learn>=1.3",
]
[project.scripts]
cbpred = "cbpred.__main__:main"
[tool.setuptools.packages.find]
include = ["cbpred*"]

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tests/__init__.py Normal file
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167
tests/test_pipeline.py Normal file
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from __future__ import annotations
import json
import tempfile
import unittest
from pathlib import Path
import numpy as np
import pandas as pd
from cbpred.pipeline import (
CSV_COLUMNS,
EIGHT_H_MS,
aggregate_8h,
build_feature_dataset,
compute_indicators,
fit_asset_scaler,
_vectorized_window_features,
_window_feature_names,
_window_features,
run_training,
)
def make_hour_rows(start_ms: int, hours: int, phase: float = 0.0):
rows = []
price = 100.0 + phase
for index in range(hours):
timestamp = start_ms + index * 60 * 60 * 1000
drift = 0.0015 * np.sin(index / 17 + phase)
open_ = price
close = price * (1 + drift)
high = max(open_, close) * 1.003
low = min(open_, close) * 0.997
rows.append((timestamp, open_, high, low, close, 1000 + index, 100000 + index * 10, 500, 10))
price = close
return rows
class PipelineTests(unittest.TestCase):
def test_aggregate_8h_drops_incomplete_buckets(self):
rows = make_hour_rows(0, 17)
frame = aggregate_8h(rows)
self.assertEqual(len(frame), 2)
self.assertEqual(frame.iloc[0]["timestamp"], 0)
self.assertAlmostEqual(frame.iloc[0]["quote"], sum(row[6] for row in rows[:8]))
def test_future_target_changes_but_past_features_do_not(self):
start = 0
raw = aggregate_8h(make_hour_rows(start, 8 * 70))
indicators = compute_indicators(raw)
profiles = {"x": {"asset_atr_price_top20_median": 0.01, "asset_quote_volume_top20_median_log": 12.0}}
scaler = fit_asset_scaler(profiles)
one, names, _ = build_feature_dataset(
{"x": raw},
1,
int(raw.iloc[55]["timestamp"]),
int(raw.iloc[-1]["timestamp"] + EIGHT_H_MS),
scaler,
volatility_rank_threshold=0.0,
)
changed = raw.copy()
changed.loc[62:, "low"] *= 0.5
two, _, _ = build_feature_dataset(
{"x": changed},
1,
int(raw.iloc[55]["timestamp"]),
int(raw.iloc[-1]["timestamp"] + EIGHT_H_MS),
scaler,
volatility_rank_threshold=0.0,
)
anchor = int(raw.iloc[60]["timestamp"])
first = one[one["timestamp"] == anchor].iloc[0]
second = two[two["timestamp"] == anchor].iloc[0]
self.assertEqual(len(names), 18 * 10 + 2)
self.assertGreater(first["target_scale"], 0.0)
for name in names:
if name != "target":
self.assertEqual(first[name], second[name])
self.assertNotEqual(first["target"], second["target"])
rising = raw.copy()
anchor_close = float(rising.iloc[60]["close"])
for index in range(61, 67):
open_ = anchor_close * (1.01 + 0.002 * (index - 61))
close = open_ * 1.002
rising.loc[index, "open"] = open_
rising.loc[index, "low"] = open_ * 0.999
rising.loc[index, "high"] = close * 1.001
rising.loc[index, "close"] = close
rising_dataset, _, _ = build_feature_dataset(
{"x": rising},
1,
int(raw.iloc[55]["timestamp"]),
int(raw.iloc[-1]["timestamp"] + EIGHT_H_MS),
scaler,
volatility_rank_threshold=0.0,
)
rising_row = rising_dataset[rising_dataset["timestamp"] == anchor].iloc[0]
self.assertGreater(rising_row["target"], 0.0)
expected_target = (
np.quantile(rising.loc[61:66, "low"].to_numpy(dtype=float), 0.20)
/ anchor_close
- 1.0
) / rising_row["target_scale"]
self.assertAlmostEqual(rising_row["target"], expected_target)
def test_vectorized_features_match_window_features(self):
raw = aggregate_8h(make_hour_rows(0, 8 * 80))
indicators = compute_indicators(raw)
asset = {
"asset_atr_price_top20_median": 0.01,
"asset_quote_volume_top20_median_log": 12.0,
}
for stage in (1, 2, 3):
names = _window_feature_names(stage)
for anchor in (18, 30, 55):
window = indicators.iloc[anchor - 17 : anchor + 1]
expected = _window_features(window, stage, asset)
actual = _vectorized_window_features(
indicators, stage, np.array([anchor]), asset
)[0]
np.testing.assert_allclose(
actual,
np.array([expected[name] for name in names]),
rtol=1e-12,
atol=1e-12,
equal_nan=True,
)
def test_three_stage_training_writes_report_and_model(self):
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
data_dir = root / "data"
raw_dir = data_dir / "raw"
raw_dir.mkdir(parents=True)
symbols = []
end_ms = 0
for idx in range(4):
symbol = f"COIN{idx}/USDT:USDT"
frame = aggregate_8h(make_hour_rows(0, 8 * 400, idx * 0.1))
path = raw_dir / f"{idx}.csv"
frame.to_csv(path, index=False)
end_ms = max(end_ms, int(frame.iloc[-1]["timestamp"] + EIGHT_H_MS))
symbols.append({"sid": idx, "symbol": symbol, "path": str(path.relative_to(data_dir)), "bars": len(frame)})
(data_dir / "manifest.json").write_text(
json.dumps({"end_ms": end_ms, "symbols": symbols})
)
report = run_training(
data_dir,
root / "artifacts",
end_ms=end_ms,
test_months=1,
n_estimators=4,
n_jobs=1,
)
self.assertIn(report["selected"]["stage"], {1, 2, 3})
self.assertEqual(len(report["stages"]), 3)
self.assertTrue((root / "artifacts" / "model.joblib").exists())
self.assertTrue((root / "artifacts" / "report.md").exists())
self.assertGreaterEqual(report["test"]["calibrated_q80_coverage"], 0.0)
self.assertEqual(report["protocol"]["volatility_filter"]["atr_rank_window"], 200)
self.assertAlmostEqual(report["protocol"]["volatility_filter"]["min_rank"], 0.0)
if __name__ == "__main__":
unittest.main()