Borrowing it
Nothing to install: this file belongs to pradeepsiddappa/indian-trading-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pradeepsiddappa/indian-trading-agent/main/CLAUDE.mdgit clone --depth 1 https://github.com/pradeepsiddappa/indian-trading-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/pradeepsiddappa/indian-trading-agent/claude-md)<a href="https://agentmods.dev/instructions/pradeepsiddappa/indian-trading-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/pradeepsiddappa/indian-trading-agent/claude-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.09394 | $0.09394 |
| Opus 5 | $0.04697 | $0.04697 |
| Sonnet 5 | $0.01879 | $0.01879 |
| Haiku 4.5 | $0.00939 | $0.00939 |
Grade A, and why
indian-trading-agent CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 573 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Indian Market Trading Agent
AI-powered multi-agent trading decision system for Indian markets (NSE/BSE). Built on TradingAgents framework with LangGraph.
Architecture
frontend/ (Next.js 16 + Tailwind + shadcn/ui + Open Sans) :3000
|
backend/ (FastAPI + WebSocket) :8000
|
tradingagents/ (LangGraph multi-agent pipeline)
|
yfinance + RSS feeds (NSE/BSE data + news)
Quick Start
# 1. Install Python deps
python3 -m venv venv && source venv/bin/activate
pip install -e .
pip install fastapi uvicorn websockets aiosqlite numpy feedparser
# 2. Configure API keys (2 options)
# Option A: via .env
echo 'ANTHROPIC_API_KEY=your_key' > .env
# Option B: via Settings page in the UI (stored in SQLite, takes priority)
# 3. Start backend
uvicorn backend.app:app --reload --port 8000
# 4. Start frontend (separate terminal)
cd frontend && npm install && npm run dev
# 5. Open http://localhost:3000
Information Architecture
The UI is organized by daily workflow (not by technical feature):
🏠 Today — Daily starting page with auto-loaded top picks + workflow guide
DISCOVER
✨ Top Picks — AI-free unified recommendation engine (combines all signals)
📡 Market Scan — Gap / Volume / Breakout scanner
🎯 Strategies — S/R, Pivot Points, Cyclical Patterns (seasonality, sector rotation)
📰 News Feed — Aggregated Indian market news (RSS + yfinance, customizable)
ANALYZE
🔍 Deep Analysis — AI-powered 10-agent pipeline (paid ~Rs.15-60)
📊 Charts — Candlestick charts with volume
VALIDATE
🏆 Performance — Historical win rate of each strategy (FREE)
🧪 Simulation — Paper trading + historical recommender backtest (FREE)
🧠 Learning Insights — Pattern analysis of YOUR past trades (FREE, no ML)
📈 Signal Performance — Per-signal win rate + auto-tune recommender (FREE)
🎯 Verdict Calibration — Is the daily verdict actually predictive? (FREE)
⚖️ Confidence Calibration — Brier score: are stated probabilities honest? (FREE)
👁️ Shadow Trades — Counterfactual auto-tracking of skipped picks (FREE)
🧠 Memory Admin — Inspect + prune agent BM25 memories (FREE)
🔬 AI Backtest — Run AI pipeline on past dates (paid)
📋 My Trades — History with P&L tracking + "Teach the agent" reflection
⚙️ Settings — API keys (UI), LLM provider switcher, model selection, cost guide
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +13 lines · +563 tokens per session 8e51bbca7a72
- 8d ago First seen · 560 lines · 8,831 tokens per session scan A a42ec1aaebae
indian-trading-agent CLAUDE.md is an instructions file published in the GitHub repository pradeepsiddappa/indian-trading-agent (23 stars, last pushed 5d ago), licensed Apache-2.0. It adds 9,394 tokens to every session, about $0.0470 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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