2004-tqx-quant

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)

1. Register at https://www.tqx.trade (email signup). A PAPER (simulation) account is provisioned automatically — all workflows below are safe to run on it.
2. Collect credentials securely: agents must use request_env_input for TQX_EMAIL and TQX_PASSWORD — never ask for credentials in chat.
3. Install + login + verify:

pip install tqx-cli
tqx-cli login --email "$TQX_EMAIL" --password "$TQX_PASSWORD"
tqx-cli --json balance        # non-error response = onboarding complete

Token is cached in ~/.tqx/config.yaml.

4. 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>          # waits and returns results

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 *            # common trading API — MANDATORY
from panda_backtest.api.stock_us_api import *   # US market data API — MANDATORY
# HK market: from panda_backtest.api.stock_hk_api import *
import tqx_data

def initialize(context):
    # Account ID is per-user — discover it once, do NOT hardcode.
    # The '8888' from CN-market docs does NOT exist for HK/US backtests.
    context.account = list(context.stock_account_dict.keys())[0]
    context.symbol = 'AAPL.NB'   # symbol format: US = TICKER.NB (NOT .US!), HK = 00700.HK
    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)
    # bar CAN be None — with a WRONG suffix it is None EVERY day (silent 0-trade run)
    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}")   # print() = strategy log; SRLog is FORBIDDEN
    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   # profit, alpha, beta, sharpe, IR
tqx-cli --json backtest_result <backtest_id> --section trade     # ⚠️ always check trades executed
tqx-cli --json backtest_result <backtest_id> --section log       # per-order rejection reasons

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'):

1. curl localhost:8090/api/health (or any GET route) returns JSON — backend alive.
2. 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.
3. Verify fills exist via backtest_result <id> --section trade — SUCCESS status with 0 trades means a symbol-suffix bug (see failure table).
4. Kill/restart server.py and confirm history persists (disk, not memory).