flox-py 0.5.3

Python bindings for the Flox indicator library

flox-py

Python bindings for the FLOX trading framework.

Install

pip install flox-py

Quick Start

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, sig.price)

runner = flox.Runner(registry, on_signal)
runner.add_strategy(SMAcross([btc]))
runner.start()
# feed market data:
# runner.on_trade(btc, price, qty, is_buy, ts_ns)
# runner.on_book_snapshot(btc, bid_prices, bid_qtys, ask_prices, ask_qtys, ts_ns)
runner.stop()

Symbol and SymbolRegistry

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

btc.id        # int, e.g. 1
btc.name      # "BTCUSDT"
btc.exchange  # "binance"
btc.tick_size # 0.01

int(btc)   # 1
print(btc) # Symbol(binance:BTCUSDT, id=1)
# Symbol objects work transparently as int anywhere an ID is expected

Strategy

Subclass flox.Strategy and override the callbacks you need.

class MyStrategy(flox.Strategy):
    def __init__(self, symbols):
        super().__init__(symbols)   # symbols: list[Symbol | int]

    def on_start(self): ...
    def on_stop(self): ...

    def on_trade(self, ctx, trade):
        # ctx  -- SymbolContext
        # trade -- TradeData
        pass

    def on_book_update(self, ctx): ...

Order emission (shorthand, uses first symbol by default)

Method	Description
market_buy(qty, symbol=None)	Market buy
market_sell(qty, symbol=None)	Market sell
limit_buy(price, qty, symbol=None)	Limit buy
limit_sell(price, qty, symbol=None)	Limit sell
stop_market(side, trigger, qty, symbol=None)	Stop market
close_position(symbol=None)	Close position (reduce-only)

SymbolContext

Property	Type	Description
position	float	Current position quantity
last_trade_price	float	Last trade price
best_bid	float	Best bid
best_ask	float	Best ask
mid_price	float	Mid price
is_flat()	bool	No position
is_long()	bool	Long position
is_short()	bool	Short position

TradeData

Property	Type	Description
symbol	int	Symbol ID
price	float	Trade price
quantity	float	Trade quantity
is_buy	bool	Buy-side aggressor
timestamp_ns	int	Timestamp (nanoseconds)

Runner

runner = flox.Runner(registry, on_signal)                  # synchronous
runner = flox.Runner(registry, on_signal, threaded=True)   # Disruptor background thread

runner.add_strategy(strategy)
runner.start()
runner.on_trade(btc, price, qty, is_buy, ts_ns)
runner.on_book_snapshot(btc, bid_prices, bid_qtys, ask_prices, ask_qtys, ts_ns)
runner.stop()

The on_signal callback receives Signal objects:

Property	Type	Description
side	str	"buy" or "sell"
quantity	float	Order quantity
price	float	Limit price (0 for market)
order_type	str	"market", "limit", etc.
order_id	int	Internal order ID

BacktestRunner

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

stats = bt.run_csv("data.csv")             # auto-detects symbol from registry
stats = bt.run_csv("data.csv", "BTCUSDT")  # explicit symbol name

Stats dict

Key	Description
return_pct	Net return percentage
net_pnl	Net P&L after fees
total_trades	Round-trip trade count
win_rate	Winning trade fraction
sharpe	Annualized Sharpe ratio
max_drawdown_pct	Peak-to-trough drawdown (%)

Modules

Module	Description
Strategy / Runner	Event-driven live and backtest strategies
Engine	Batch backtest engine (signal arrays, parallel runs)
Indicators	EMA, SMA, RSI, MACD, ATR, Bollinger, ADX, Stochastic, CCI, VWAP, CVD, and more
Aggregators	Time, tick, volume, range, renko, Heikin-Ashi bars
Order Books	N-level, L3, cross-exchange CompositeBookMatrix
Profiles	Footprint bars, volume profile, market profile
Positions	Position tracking with FIFO/LIFO/average cost basis
Replay	Binary log reader/writer, market data recorder

All compute-heavy operations release the GIL for true parallelism.

Full API reference at flox-foundation.github.io/flox/reference/python.