| name | @2004/tqx-quant |
| version | 2.3.1 |
| description | TQX (tqx.trade) HK/US stock quant workflow via tqx-cli: cross-sectional factor analysis, event-driven strategy backtests on the panda_backtest engine, and agent-driven automated paper trading.
Use when the user wants to run factor IC/IR analysis, backtest a Python trading strategy on Hong Kong or US stocks, or set up agent-automated trading (e.g. "backtest a moving-average strategy on AAPL", "analyze a momentum factor on HK stocks", "let the agent trade my paper account").
|
| author | starchild-2004 |
| tags | ["quant","backtest","factor-analysis","stocks","tqx","trading"] |
TQX Quant — Factor Analysis & Strategy Backtest
TQX (https://www.tqx.trade) is a HK/US stock quant platform. This skill drives it through the official tqx-cli pip package. Everything below was verified end-to-end against the live service.
Official TQX skills (source of truth — read these first)
TQX publishes two official skills. Fetch the raw URLs directly (the repo UI is a JS SPA; only /-/git/raw/ URLs return content):
1. tqx-research — factor analysis, strategy backtest, workflow management (Python tqx-cli, email/password login):
- Main:
https://cnb.cool/liangyunzhijing/clis/-/git/raw/main/skills/tqx-research/SKILL.md
- References (read before generating code — do NOT guess APIs):
- commands:
.../skills/tqx-research/references/commands.md
- strategy templates:
.../skills/tqx-research/references/strategy_templates.md
- US API:
.../skills/tqx-research/references/stock_us_api.md
- HK API:
.../skills/tqx-research/references/stock_hk_api.md
- tqx_data:
.../skills/tqx-research/references/tqx_data_usage.md
(replace ... with https://cnb.cool/liangyunzhijing/clis/-/git/raw/main)
2. tqx-trading — account/positions/orders/trades queries + authorized order placement (TypeScript @tqx-ai/cli@0.1.3, API-key auth, pin the version):
- Main:
https://cnb.cool/liangyunzhijing/clis/-/git/raw/main/skills/tqx-trading/SKILL.md
- OpenAPI:
https://www.tqx.trade/openapi/v1/openapi.json (interactive: /openapi/v1/scalar)
Routing rule: research/backtest/factor work → tqx-research (tqx-cli, Python). Live account state & order execution → tqx-trading (tqx, TS CLI). They use DIFFERENT CLIs and DIFFERENT auth (email login vs TQX_API_KEY) — don't mix them. This skill below adds what the official docs don't cover: verified onboarding, failure-mode table, and agent-automation patterns.
User onboarding (first-time setup, ~3 minutes)
- Register at https://www.tqx.trade (email signup). A PAPER (simulation) account is provisioned automatically — all workflows below are safe to run on it.
- Collect credentials securely: agents must use
request_env_input for TQX_EMAIL and TQX_PASSWORD — never ask for credentials in chat.
- Install + login + verify:
pip install tqx-cli
tqx-cli login --email "$TQX_EMAIL" --password "$TQX_PASSWORD"
tqx-cli --json balance
Token is cached in ~/.tqx/config.yaml.
- First quick win (recommended demo): run the "5d momentum" factor analysis from §1 below — completes in ~1–2 min and produces IC/IR/Sharpe numbers you can show immediately.
⚠️ Token expiry gotcha (verified): both accessToken AND refresh_token can expire together. Do NOT only match one specific error string — re-login on ANY response containing LOGIN_REQUIRED, 均已失效, or Please log in to continue. A strict matcher silently fails and every later call returns auth errors.
CLI command map
factor_create / factor_run / factor_result / factor_list / factor_delete
strategy_create / strategy_run / strategy_result / strategy_list / strategy_delete
backtest_result # per-backtest detail: summary/account/position/profit/trade/log sections
workflow_list / workflow_stop / balance
Add --json for machine-readable output.
1. Factor analysis (cross-sectional IC/IR)
tqx-cli --json factor_create --market us --name "5d momentum" \
--formula "close/ref(close,5)-1" \
--start-date 20250101 --end-date 20250701 --group-number 2
tqx-cli --json factor_run <factor_id>
Result parsing gotcha: the result JSON has TWO formats depending on backend version — legacy nodes[].result_json and current root-level factor_analysis. Handle both. Key metrics: IC mean, IR, t-stat, annualized group returns, Sharpe.
2. Strategy backtest (panda_backtest engine)
tqx-cli --json strategy_create --market us --name "AAPL SMA cross" \
--code "$(cat strategy.py)" \
--start-date 20250101 --end-date 20250701 \
--start-capital 1000000 --commission-rate 0.0003 --slippage 0.001 --frequency 1d
tqx-cli --json strategy_run <strategy_id>
Strategy code contract (ALL verified the hard way)
from panda_backtest.api.api import *
from panda_backtest.api.stock_us_api import *
import tqx_data
def initialize(context):
context.account = list(context.stock_account_dict.keys())[0]
context.symbol = 'AAPL.NB'
context.closes = []
def handle_data(context, data):
account = context.stock_account_dict.get(context.account)
if account is None:
return
bar = data.get(context.symbol)
if bar is None or getattr(bar, 'close', None) is None or float(bar.close) <= 0:
return
price = float(bar.close)
context.closes.append(price)
if len(context.closes) < 20:
return
fast = sum(context.closes[-5:]) / 5
slow = sum(context.closes[-20:]) / 20
position = account.positions.get(context.symbol)
quantity = 0 if position is None else position.quantity
sellable = 0 if position is None else position.sellable
if fast > slow and quantity == 0:
buy_qty = int(account.cash * 0.9 // price)
if buy_qty > 0:
order_shares(context.account, context.symbol, buy_qty, style=MarketOrderStyle)
print(f"BUY {buy_qty} @ {price:.2f}")
elif fast < slow and quantity > 0 and sellable > 0:
order_shares(context.account, context.symbol, -sellable, style=MarketOrderStyle)
print(f"SELL {sellable} @ {price:.2f}")
Verified live (2026-07): this exact code on AAPL.NB, 20250101–20251231, produced 18 real fills (check with backtest_result <backtest_id> --section trade --all-pages).
Hard rules learned from real failures:
| Symptom | Root cause | Fix |
|---|
禁止使用危险函数 dir() | Security filter blocks introspection | Never use dir()/eval()/exec; to inspect context, raise Exception(str(...)) and read the error_detail in run logs |
访问了不存在的键 on order | Wrong account ID (e.g. '8888') | Use list(context.stock_account_dict.keys())[0] |
order_shares() missing 1 required positional argument | Called with 2 args | Signature is order_shares(account, symbol, quantity) |
股票X不属于当前股票回测市场 | Wrong symbol suffix | US = TICKER.NB (NOT .US!), HK = XXXXX.HK; .O/.N/bare tickers are rejected |
| Backtest SUCCESS but 0 trades, 0 log lines, profit = 0.0 | Symbol suffix .US (or any wrong suffix) → every bar returns None → defensive guard skips all days silently | Use TICKER.NB for US stocks. ALWAYS verify via backtest_result <id> --section trade — SUCCESS ≠ trades executed |
| Run FAILED immediately (~0.3s, node failed) | SRLog is not a valid API in strategy code | Use plain print() for strategy logging (visible in --section log) |
| Run status FAILED but NO failed node (all nodes success/pending) | Transient TQX queue/scheduler error, not your code | Resubmit the same workflow once — typically succeeds in ~30s. Only debug strategy code if a node actually failed |
Backtest SUCCESS but 0 trades, 标的不在当前回测数据集内 | Date range beyond ingested market data (recent months may not be loaded even though the benchmark series exists) | Shift the window earlier (e.g. use last year's range); verify trades>0 in the trade section before trusting metrics |
frequency rejected | Only 1d and 1M are valid | — |
Reading results
strategy_run/strategy_result returns run status + node outputs. For full detail, extract the backtest id from run logs (BacktestNodeIdentifier: line) or node output, then:
tqx-cli --json backtest_result <backtest_id> --section summary
tqx-cli --json backtest_result <backtest_id> --section trade
tqx-cli --json backtest_result <backtest_id> --section log
A run can report SUCCESS with zero trades (orders silently rejected day by day). Always confirm the trade section is non-empty before reporting performance numbers.
Debugging failed runs
Error details are NOT in the top-level status — fetch run logs and read error_detail, which includes the exact strategy line number and exception message:
from tqx_cli.config import load_config
from tqx_cli.auth import require_login
from tqx_cli.workflow import get_run_logs
cfg = load_config(); token, uid, _ = require_login(cfg, cfg.get("_config_path"))
for l in get_run_logs(cfg, token, uid, run_id).get("logs") or []:
if l.get("error_detail"): print(l["error_detail"])
Cost & pacing
Backtests are billed in TQX compute credits (tqx-cli balance). A 6-month daily-frequency single-stock backtest takes ~2 minutes wall time. Poll strategy_result every 3s; don't fire concurrent runs of the same workflow.
3. Templates (copy-paste starting points)
T1 — Momentum factor (cross-sectional, whole market)
tqx-cli --json factor_create --market us --name "5d momentum" \
--formula "close/ref(close,5)-1" \
--start-date 20250101 --end-date 20250701 --group-number 5
Other verified formulas: mean reversion -(close/ref(close,5)-1), volume surge volume/mean(volume,20).
Factor mode is whole-market cross-sectional only (--market hk|us) — it CANNOT target one stock; for single-stock questions use a strategy backtest (T2).
T2 — Single-stock backtest (SMA cross on AAPL)
Use the strategy code contract in §2 verbatim — it IS the template. Change context.symbol and the signal logic only. Keep the None-bar guard and dynamic account discovery.
T3 — Agent-driven automated trading loop (paper account)
Pattern verified over a 10-round live run (~21 min, end-to-end):
loop every N minutes:
1. fetch live positions + account state
2. compute signal (factor value or strategy rule)
3. decide: buy / sell / hold ← agent reasoning step
4. place order (paper account, small fixed qty during development)
5. journal the decision: {ts, reasoning, tool_calls, params, result, position_delta}
Hard rules for automation:
- PAPER account only until the user explicitly approves real trading; cap order size during development.
- Journal every decision (JSONL is enough) — users must be able to audit why each trade happened.
- Re-login on ANY auth-ish error string (see token gotcha above); a mid-loop token expiry must self-heal, not kill the loop.
- Check compute balance before each backtest-class call to avoid silent overdraft.
T4 — Studio: ready-to-run companion UI (templates/studio/)
A complete, tested web workbench ships with this skill — do NOT build a dashboard from scratch. Copy templates/studio/ into the user's workspace, start it, and adapt.
Files:
| File | Role |
|---|
server.py | Stdlib HTTP backend (port 8090, no pip deps). Proxies tqx-cli, auto re-login on token expiry, serves all /api/* routes |
index.html | Single-file frontend: factor analysis, backtest submit/history, positions, agent decision timeline |
agent.py | Agent trading loop (LLM via proxied_post + tool calls), JSONL decision journal |
backtests.py / strategies.py / journal.py | Disk persistence modules (see data spec below) |
styles/index.html | Alternate theme variant for user selection |
Run: python3 server.py from the studio dir (background), then preview(action="serve") on it. Credentials come from TQX_EMAIL / TQX_PASSWORD env vars (collect via secure input — never hardcode).
Design spec (keep when restyling):
- Minimalist quant-tech aesthetic: dark-first, monospace numerals, dense tables, no decorative graphics. Bright/cyberpunk themes have been explicitly rejected by users.
- History rows must be loadable: clicking 载入 on any history record re-displays its full result in the top result area (factor runs → factor metrics/charts via
loadBt; strategy runs → dedicated strategy-result card via loadStgBt: return vs benchmark, annualized, Sharpe, max drawdown, trade count, params + code link). A history table that only lists rows without load-back is incomplete.
- UI exposes only high-frequency params (formula, market, date range, groups, rebalance, direction) + account switcher + NAV curve + decision timeline. Everything complex (custom strategy code, stock pools, commission/slippage) stays in the conversation layer — the agent has the full CLI surface, the UI must not duplicate it.
- Amounts: format gold/cash values with a single dedicated formatter (a past bug: two formatters colliding produced wrong displays). Check dark-mode contrast on every card.
Data storage spec:
data/backtests/index.json + one JSON per run — MUST persist the full strategy code string (not a truncated preview) so any run can be reloaded and re-edited.data/journal/*.jsonl — one line per agent decision, tagged source: manual | agent, append-only, never rewritten.data/strategies/ — named saved strategies. All state is plain JSON on disk; no DB.
Testing spec (before declaring the studio 'working'):
curl localhost:8090/api/health (or any GET route) returns JSON — backend alive.- Submit one REAL backtest through the UI and confirm it appears in the history panel with full code + metrics. A rendered page alone is NOT verification.
- Verify fills exist via
backtest_result <id> --section trade — SUCCESS status with 0 trades means a symbol-suffix bug (see failure table).
- Kill/restart
server.py and confirm history persists (disk, not memory).
