gahow/quant-trading
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(books): selected_2026_09 adds trend_rider, $2/¥10 per order, CN ¥100k

Browse Source 
Adds five trend_rider ETF strategies (best 10-year Sharpe of each version,
plus v7_vt36): 31 strategies in all. Fees per order are $2 and ¥10, and CN
accounts start with ¥100,000. No trade has been executed yet (US accounts
hold only this morning's orders; CN starts 2026-09-28).

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
2026-09-25 22:13:49 +08:00
parent 5dbabc3e08
commit 465189ba9a
2 changed files with 8 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
CLAUDE.md
View File

@@ -73,7 +73,7 @@ uv run python -m unittest discover tests
**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Orders: whole A-share lots are allocated nearest-lot within cash (`_allocate_lots`); a held name is resized only when ≥ `RESIZE_BAND` (10%) off target. Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`, 32 factor-combo strategies (`fc_{signal}_{freq}` — see `strategies/factor_combo.py`), and the robust-momentum bar strategies `overnight_recovery_top10`, `lowrisk_momentum_top15`, `lowbeta_sharpe_mom_top20`.
**Live books** (`trader.LIVE_BOOKS`): a book is a cohort of strategies, each with its own account, started on one date with one capital and a flat fee per order. The original book (every live strategy, `MONITOR_CAPITAL`, `MARKET_FEES`) keeps state in `data/trader_*.json`; a named book keeps it in `data/books/<book>/`. The monitor runs every book; `auto`/`status`/`log`/`compare`/`replay` and `bridge.py live` take `--book`, and `bridge.py books` lists them for the UI's book selector. `selected_2026_09` (26 strategies, US $10,000 / CN ¥50,000, $1 / ¥5 per order) starts on 2026-09-25 (US) and 2026-09-28 (CN).
**Live books** (`trader.LIVE_BOOKS`): a book is a cohort of strategies, each with its own account, started on one date with one capital and a flat fee per order. The original book (every live strategy, `MONITOR_CAPITAL`, `MARKET_FEES`) keeps state in `data/trader_*.json`; a named book keeps it in `data/books/<book>/`. The monitor runs every book; `auto`/`status`/`log`/`compare`/`replay` and `bridge.py live` take `--book`, and `bridge.py books` lists them for the UI's book selector. `selected_2026_09` (31 strategies, US $10,000 / CN ¥100,000, $2 / ¥10 per order) starts on 2026-09-25 (US) and 2026-09-28 (CN).
**Strategy protocol** (`strategies/base.py`): All strategies inherit from `Strategy` ABC and implement `generate_signals(data) → DataFrame` where the returned DataFrame contains portfolio weights (rows = dates, columns = assets, values sum to ~1.0 per row). Each strategy is responsible for applying its own 1-day lag via `.shift(1)` to avoid lookahead bias — the backtest engine does not shift. A `BarStrategy` declares `fields` (close/open/high/low/volume) and receives `{field: DataFrame}`; live it uses completed bars only (row d from bars through d-1, traded at the close of d), while close-price strategies read the session's open as their latest price.

10
trader.py
View File

@@ -118,13 +118,15 @@ LIVE_BOOKS: dict[str, dict] = {
# CN starts with the first session after the strategies were chosen
# (the choice was made after the CN close of 2026-09-25).
"start": {"us": "2026-09-25", "cn": "2026-09-28"},
"capital": {"us": 10_000.0, "cn": 50_000.0},
"fee_per_order": {"us": 1.0, "cn": 5.0},
"capital": {"us": 10_000.0, "cn": 100_000.0},
"fee_per_order": {"us": 2.0, "cn": 10.0},
# Chosen on 2026-09-25: beat the benchmark in the Apr-Sep 2026 replay
# (point-in-time members) AND passed a 10-year PIT backtest (US:
# Sharpe >= 0.6, MaxDD > -50%; CN: Sharpe >= 0.2, CAGR > 0); no daily
# variants; at most two frequencies per factor signal. Plus the three
# strategies from the 2026-09 PIT research.
# strategies from the 2026-09 PIT research, and the trend_rider ETF
# strategies (lost 7-12% in Apr-Sep, 10-year Sharpe 0.8-1.4): the best
# 10-year Sharpe of each version, and v7_vt36 (the V7+VT36 champion).
"strategies": {
"us": [
"fc_up_cap_quality_mom_biweekly", "fc_up_cap_quality_mom_weekly",
@@ -137,6 +139,8 @@ LIVE_BOOKS: dict[str, dict] = {
"fc_rec_mfilt_ma200_monthly", "fc_rec_mfilt_ma200_biweekly",
"hybrid_alpha_top10", "recovery_mom_top10",
"overnight_recovery_top10",
"trend_rider_v7_vt24", "trend_rider_v7_vt36", "trend_rider_v3_vt24",
"trend_rider_v5_vt30", "trend_rider_v4",
],
"cn": [
"fc_up_cap_mom_gap_monthly", "fc_up_cap_mom_gap_biweekly",