nlethetech

nlethetech / nepse-quant-terminal

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Terminal-based NEPSE quant trading dashboard with paper trading, backtesting, analytics, and AI agent support.

nepsequant.com
101
61
100% credibility
Found Apr 20, 2026 at 101 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A terminal-based dashboard for practicing quantitative trading on Nepal's stock exchange with simulated portfolios, signals, backtesting, and market views.

How It Works

1
🔍 Discover safe stock practice

You find a free terminal app to practice trading Nepal stocks without real money risk.

2
📥 Get the app ready

Download it and run a quick setup to load Nepal stock history into your practice world.

3
🚀 Launch your dashboard

Open the app to see live Nepal market quotes, movers, and signals right in your terminal.

4
💰 Set up practice money

Create a virtual portfolio with pretend cash to buy and sell stocks safely.

5
📈 Spot trading ideas

Review smart buy/sell signals, backtests, and market scans to pick winners.

6
🛒 Make pretend trades

Buy or sell stocks in your practice account, with auto-trading if you want hands-off help.

✅ Track your wins safely

Watch your virtual profits grow, learn from losses, and master Nepal trading risk-free.

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Star Growth

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AI-Generated Review

What is nepse-quant-terminal?

This Python project delivers a terminal-based NEPSE quant trading dashboard for the Nepal Stock Exchange, packing paper trading, backtesting, analytics, and AI agent support into a no-browser TUI. It simulates full portfolios with P&L tracking, auto-trading engines, and live market views—pulling NEPSE data into a Bloomberg-style interface for strategy testing without real broker risks. Setup grabs historical data via a quick script, letting you paper trade or backtest immediately.

Why is it gaining traction?

Unlike generic quant libs, it's NEPSE-native with tailored signals like hydropower satellite data and gold hedge overlays, plus rigorous stats like deflated Sharpe and Monte Carlo validation. The local AI agent (Ollama or Claude) analyzes portfolios on-demand, and strategy builder lets you tweak signals for auto-trading—standing out for devs wanting a ready-to-run terminal dashboard over piecing together scrapers and backtesters.

Who should use this?

NEPSE traders paper-testing quant strategies before live deployment, or Nepali quants building custom signals without cloud dependencies. Ideal for hobbyists importing MeroShare holdings to simulate multi-account trading, or analysts running walk-forward backtests on 6+ years of local data.

Verdict

Grab it if you're in NEPSE—solid for paper trading and prototyping at 101 stars, though 1.0% credibility flags early maturity with thin tests. Run `python setup_data.py` and dive in; pair with daily scrapes for production signals.

(187 words)

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