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Python quickstart

Requirements

  • Python 3.10+

1. Install

pip install flox-py

2. First indicator

import flox_py as flox

ema = flox.EMA(20)

prices = [100 + i * 0.1 for i in range(50)]
for price in prices:
    value = ema.update(price)
    if value is not None:
        print(f"EMA(20): {value:.4f}")

All streaming indicators return None during warmup and a float once ready. Check .ready to test without calling update.

3. First strategy

import flox_py as flox

registry = flox.SymbolRegistry()
btc = registry.add_symbol("binance", "BTCUSDT", tick_size=0.01)

class SMACross(flox.Strategy):
    def __init__(self, symbols):
        super().__init__(symbols)
        self.fast = flox.SMA(10)
        self.slow = flox.SMA(30)

    def on_trade(self, ctx, trade):
        f = self.fast.update(trade.price)
        s = self.slow.update(trade.price)
        if f is None or s is None:
            return
        if f > s and ctx.is_flat():
            self.market_buy(0.01)
        elif f < s and ctx.is_long():
            self.close_position()

def on_signal(sig):
    print(sig.side, sig.order_type, sig.quantity)

runner = flox.Runner(registry, on_signal)
runner.add_strategy(SMACross([btc]))
runner.start()

# Feed market data from your source:
# runner.on_trade(btc, price, qty, is_buy, ts_ns)

runner.stop()

4. Backtest

Two paths. Pick the second by default.

Bare backtest

bt = flox.BacktestRunner(registry, fee_rate=0.0004, initial_capital=10_000)
bt.set_strategy(SMACross([btc]))

stats = bt.run_csv("btcusdt_trades.csv")
print(stats["return_pct"], stats["sharpe"], stats["max_drawdown_pct"])

CSV format: timestamp,price,qty,is_buy, one trade per row. Flat fee rate, no funding, no liquidation, no rate limits, no queue position. Use for an indicator sanity check, not for a decision about real capital.

Realistic backtest

stack = flox.VenueStack.binance_um_futures(account_id=42, equity=10_000.0)
acct = stack.account()
liq = stack.liquidation()
fees = stack.fees()
funding = stack.funding()
exec_ = stack.executor()

One call wires the cross-margin Account, the MM tier ladder + ADL ranking, the 30d VIP fee schedule (bound to the account), the funding settlement schedule, the venue-specific rate-limit policy, and the venue-availability hook. Other factories: bybit_linear, okx_swap, deribit. For custom venues see flox.assemble_custom_venue(...).

Full pattern: Realistic backtest in one call.

Building from source

Use this if you need the current main branch before a release is published.

Requirements

  • GCC 14+ or Clang 18+
  • CMake 3.22+
  • Python 3.10+
git clone https://github.com/FLOX-Foundation/flox.git
cd flox
pip install scikit-build-core pybind11 numpy
pip install ./python

That's it — scikit-build-core handles the cmake build internally. The module installs into your environment the same way as the PyPI package.

Next steps