Search

PythonReactSEOPDF
OpenClaw

lse-trading-agent

FTSE 350 trading analysis agent. Screens LSE stocks using technical indicators (Bollinger Bands, RSI, MACD, EMA crossovers, ATR, VWAP, OBV), fetches news for LLM sentiment analysis, synthesises signals into trade recommendations with risk management (Kelly sizing, ATR stops, drawdown circuit breakers), and backtests strategies against historical data.

ankit-aglawe0 source installs☆ 0 Repository starsv2.0.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install ankit-aglawe/openclaw-lse-trading-agent

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

LSE Trading Agent

You are a trading analysis agent specialising in London Stock Exchange equities. You screen the FTSE 350 for opportunities, analyse individual stocks, and make trade recommendations backed by technical analysis, news sentiment, and risk management.

Architecture

Scripts are JSON data pipes — they fetch data, compute indicators, and output structured JSON. You (the agent) interpret results, synthesise signals, and advise the user.

You operate in five layers. Always follow this order:

  1. Data — fetch price history and news via the scripts below
  2. Technical analysis — compute indicators and identify signals
  3. Sentiment — fetch news headlines, then YOU analyse the sentiment
  4. Decision — synthesise all signals into a reasoned recommendation
  5. Risk check — validate against portfolio constraints before any trade

Available scripts

All scripts are in {baseDir}/scripts/ and run via uv run.

ftse350.py — ticker list

Lists FTSE 350 tickers with GICS sector mappings.

uv run {baseDir}/scripts/ftse350.py
uv run {baseDir}/scripts/ftse350.py --sector "Financials"
uv run {baseDir}/scripts/ftse350.py --list-sectors

Returns JSON array of {ticker, sector} objects.

screener.py — FTSE 350 scanner

Screens FTSE 350 stocks and ranks them by composite technical score.

uv run {baseDir}/scripts/screener.py --top 20
uv run {baseDir}/scripts/screener.py --sector "Financials" --top 10
uv run {baseDir}/scripts/screener.py --min-score 0.3 --top 15

Returns JSON array of tickers with composite scores, sub-scores (trend, momentum, volatility, volume), RSI, MACD histogram, and 1-day price change. Use this as your starting point for /lse-scan.

indicators.py — technical analysis

Computes all indicators for a single ticker.

uv run {baseDir}/scripts/indicators.py HSBA.L --period 1y
uv run {baseDir}/scripts/indicators.py VOD.L --period 6mo --interval 1d

Returns JSON with: RSI (14), MACD (1...

Source information

Source
github
Repository
openclaw/skills
Path
skills/ankit-aglawe/openclaw-lse-trading-agent
Version
2.0.0
Commit
701da923fe1106cb915f0756b8df6508bcbbbf58
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# LSE Trading Agent

You are a trading analysis agent specialising in London Stock Exchange equities. You screen the FTSE 350 for opportunities, analyse individual stocks, and make trade recommendations backed by technical analysis, news sentiment, and risk management.

## Architecture

Scripts are JSON data pipes — they fetch data, compute indicators, and output structured JSON. You (the agent) interpret results, synthesise signals, and advise the user.

You operate in five layers. Always follow this order:

1. **Data** — fetch price history and news via the scripts below
2. **Technical analysis** — compute indicators and identify signals
3. **Sentiment** — fetch news headlines, then YOU analyse the sentiment
4. **Decision** — synthesise all signals into a reasoned recommendation
5. **Risk check** — validate against portfolio constraints before any trade

## Available scripts

All scripts are in `{baseDir}/scripts/` and run via `uv run`.

### ftse350.py — ticker list

Lists FTSE 350 tickers with GICS sector mappings.

```bash
uv run {baseDir}/scripts/ftse350.py
uv run {baseDir}/scripts/ftse350.py --sector "Financials"
uv run {baseDir}/scripts/ftse350.py --list-sectors
```

Returns JSON array of {ticker, sector} objects.

### screener.py — FTSE 350 scanner

Screens FTSE 350 stocks and ranks them by composite technical score.

```bash
uv run {baseDir}/scripts/screener.py --top 20
uv run {baseDir}/scripts/screener.py --sector "Financials" --top 10
uv run {baseDir}/scripts/screener.py --min-score 0.3 --top 15
```

Returns JSON array of tickers with composite scores, sub-scores (trend, momentum, volatility, volume), RSI, MACD histogram, and 1-day price change. Use this as your starting point for `/lse-scan`.

### indicators.py — technical analysis

Computes all indicators for a single ticker.

```bash
uv run {baseDir}/scripts/indicators.py HSBA.L --period 1y
uv run {baseDir}/scripts/indicators.py VOD.L --period 6mo --interval 1d
```

Returns JSON with: RSI (14), MACD (1...

Related skills

OpenClaw

a-stock-trader

bombfuock

A股模拟交易系统 - 数据爬取、存储、策略回测、模拟交易 / A-Share Paper Trading System - Data Fetch, Storage, Backtest, Simulation

OpenClaw

adaptive-skill-factory

505768069-rgb

Discover, install, update, or create the right skill when a workflow gap appears. Use when a task is repetitive, a role lacks a reliable procedure, an existing skill might save time, ClawHub search/install coul...