PaperTrade-India 0.1.0.dev0

Production-grade simulated broker for NSE/BSE paper trading

PaperTrade-India

A production-grade simulated broker for NSE & BSE paper trading.

Realistic Indian-market order execution — fees, T+1 settlement, tick/lot/band rules, and live prices — behind a clean, Alpaca-style Python API.

Quickstart · Examples · Report a bug

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Overview

No Indian broker offers a programmatic paper-trading API. PaperTrade-India fills that gap: a simulated NSE/BSE broker that models the parts of real trading that actually move your P&L — statutory fees, T+1 settlement, price bands, slippage, and market impact — while exposing the same method surface as an Alpaca-style trading client, so you can build and test strategies without touching real capital or rewriting your code.

from papertrade_india import IndiaPaperBroker

broker = IndiaPaperBroker(initial_capital=1_000_000)
order = broker.buy("RELIANCE", 10)
print(order.filled_avg_price, order.fees_paid)

Status: alpha (pre-1.0). The public API is stable enough to build on, but minor releases may introduce breaking changes until 1.0.

Why

Platform	Paper trading	API access
Zerodha Kite	❌	❌
Upstox	⚠️ mock only	⚠️ mock only
Angel One SmartAPI	❌	❌
Dhan / Fyers / Shoonya	❌	❌
papertrade-india	✅	✅

Features

Execution realism — all on by default, so a fresh broker behaves like a real retail account:

• Indian fee model — brokerage, STT, exchange charges, GST, SEBI charges, stamp duty, and DP charges, configurable per broker and date-versioned for mid-year statutory changes.
• T+1 settlement with deliverable-quantity enforcement, plus same-day intraday round-trips via ProductType.INTRADAY and 15:15 auto-square-off.
• Tick / lot / price-band rules — orders snap to the symbol's tick and are rejected outside the daily band.
• Order types — market, limit, STOP_MARKET, STOP_LIMIT, and BRACKET with full OCO semantics.
• Synthetic L2 order book — uses real provider depth when available, with queue-position tracking and Almgren-style market impact.
• Slippage, latency, and random-rejection simulation for stress-testing strategy robustness.
• Mark-to-bid valuation for realistic unrealized P&L.

Data — a pluggable MarketDataProvider layer:

• Built-in providers: yfinance, jugaad-data, stooq, nse-bhavcopy (official EOD), nsepython, alphavantage, twelvedata, finnhub, plus live broker feeds upstox and dhan (real bid/ask + market depth).
• Per-provider circuit breakers, median aggregation across sources, a first-wins resilient_feed() helper, and a name registry.
• Live NSE/BSE holiday calendar from the exchange-published API, cached with an offline fallback.

Engineering & safety

• Thread-safe SQLite persistence (WAL, atomic transactions, versioned migrations).
• Session-phase awareness (PRE_OPEN / REGULAR / POST_CLOSE / CLOSED).
• Risk controls: kill switch, symbol whitelist, per-order and per-position notional caps.
• Idempotency keys, an immutable cash ledger with an invariant check, broker fee presets (Zerodha, Upstox, Groww, Angel One, ICICIdirect), a symbol master with delisting, corporate actions (splits + dividends), and a persisted event log with an in-process observability bus.
• Multi-account support, an optional CLI, and an optional MCP server so LLM agents can trade through tool calls.

Installation

pip install papertrade-india               # core
pip install 'papertrade-india[jugaad]'     # + NSE-direct fallback data
pip install 'papertrade-india[cli]'        # + Typer/Rich CLI
pip install 'papertrade-india[mcp]'        # + MCP server
pip install 'papertrade-india[dev]'        # + tests and linting

Requires Python 3.10+.

Quickstart

from papertrade_india import IndiaPaperBroker

broker = IndiaPaperBroker(initial_capital=1_000_000)

# Market buy (inside NSE trading hours)
order = broker.buy("RELIANCE", 10)
print(order.filled_avg_price, order.fees_paid)

# Inspect state
for position in broker.get_positions():
    print(position.symbol, position.qty, position.unrealized_pl)

account = broker.get_account()
print(f"Equity: ₹{account.equity:,.2f}  Cash: ₹{account.cash:,.2f}")

# Realize P&L
broker.sell("RELIANCE", 10)

The full getting-started walkthrough and a worked example for every feature — limit orders, multiple accounts, risk controls, live data feeds, realism configuration, and more — live in QUICKSTART.md. Runnable scripts are in examples/.

Configuration

Provide your own broker credentials and data-provider keys via a .env file — see .env.example. Everything is optional: with no keys set, the broker uses the free yfinance → jugaad-data fallback chain and works out of the box. API keys unlock live broker feeds and higher-rate data sources.

Fee model

Defaults match a typical discount-broker delivery account:

Component	Default rate	Applies to
Brokerage	₹0	Both sides (delivery)
STT	0.1%	Both sides
Exchange charge	0.00322% (NSE) / 0.00375% (BSE)	Both sides
GST	18% on (brokerage + exchange)	Both sides
SEBI charges	₹10 per crore	Both sides
Stamp duty	0.015%	Buy only
DP charge	₹13.5	Sell only

Override any field via FeeConfig, or use a named preset, to model intraday or full-service brokers.

Scope & limitations

The simulator is intentionally focused on cash equity, delivery, long-only trading. It is a faithful behavioral simulator, not an exchange replica:

Limitation	Notes
Synthetic order book, not a real matching engine	Uses real provider depth when available; accurate for retail order sizes. True queue priority isn't reproducible from retail data.
Snapshot prices, not a tick stream	Fills price off the latest snapshot — negligible at daily/swing cadence.
Corporate actions applied manually	Via apply_split / apply_dividend.
No margin, leverage, or short selling	Cash, long-only by design.
No options or F&O	Equity only.

Contributing

Issues and pull requests are welcome. Install the dev extras and run the test suite before submitting:

pip install -e '.[dev]'
pytest
ruff check src tests

License

MIT — use it however you want.

Disclaimer

This is a simulated broker; it does not place real trades. It is not investment advice. Always verify calculations against your actual broker's contract notes before relying on the simulator's outputs for tax, compliance, or investment decisions.