quant-trading-cn_skill

This skill guides you to build, backtest, and deploy quantitative trading robots leveraging practical Indian market experience.

Python

2.6k

GitHub Stars

4

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill quant-trading-cn

_meta.json292 B
package.json271 B
README.md410 B
SKILL.md4.2 KB

Overview

This skill is a quantitative trading expert built from extensive live experience in the Indian equity market, adapted to support strategy generation, backtesting, and live trading integrations (Zerodha and A-share adaptions). It bundles a guided wizard, code analysis checks, market-specific nuances, performance optimizations, and 16 domain knowledge areas to help build robust systematic strategies. The content emphasizes reproducibility between backtest and live trading and practical fixes for common production failures.

How this skill works

The skill provides an interactive wizard to generate or enhance trading bots, scripts to fetch stock universes, and a code-checker that flags common implementation issues (e.g., tick-size rounding, VWAP reset). It documents trading logic, risk rules, performance optimizations (Parquet, Polars), and market-specific behaviors so you can align backtests with live execution. Example fixes and benchmarks show where performance and consistency gains are realized.

When to use it

Building a new algorithmic trading bot for Indian markets or adapting to A-share markets
Diagnosing backtest vs live discrepancies (VWAP, data alignment, tick size)
Optimizing data workflow and backtest speed for large universes
Adding Zerodha-compatible execution and position accounting
Performing pre-deployment code checks to avoid common production failures

Best practices

Always round prices to the instrument tick size before placing orders
Reset intraday VWAP and other cumulative indicators at market open
Cache market data in Parquet and use vectorized libraries (Polars) for speed
Model trading costs and latency; simulate T vs T-1 alignment for fills
Implement structured logging and post-trade attribution for debugging

Example use cases

Generate a momentum or VWAP-pullback robot via the interactive wizard
Run a fast portfolio backtest for Nifty 50 using Parquet-cached data
Analyze an existing bot to find tick-size, VWAP, or cooldown issues
Adapt strategy parameters and market timings for A-share exchanges
Benchmark and optimize indicator computation with Polars vectorization

FAQ

Yes—Zerodha integration guidance and execution patterns are included; adapters are needed for other brokers and A-share platforms.

How does it reduce backtest-to-live mismatches?

It prescribes daily resets for intraday indicators (VWAP), enforces tick-size rounding, aligns T vs T-1 data, and models transaction costs and latency.