flox-py 0.6.0

Python bindings for the Flox indicator library

flox-py

Python bindings for FLOX, a modular framework for building trading systems with polyglot strategy bindings and AI-friendly developer tools. Same strategy code goes from backtest to live without a rewrite step.

Install

pip install flox-py

Scaffold a new project

flox new my-strategy                                   # research scaffold (default)
flox new my-bot --template=live                        # live trading via CcxtBroker
flox new my-indicators --template=indicator-library    # publishable indicator package
flox templates                                         # list templates

flox new ships with the wheel; see docs/how-to/flox-new.md for what each template lays down.

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()

Run a paper engine in one command

For tier-5/6 control (live order placement, position queries, kill switch over HTTP), flox engine sim boots a Runner + SimulatedExecutor + ControlServer + state-snapshot writer:

flox engine sim --strategy strategy.py --tape ./tape

Prints the engine URL and a copy-pasteable flox-mcp init --engine-url URL --token T command for wiring into AI tools.

AI companion

flox-mcp is a Model Context Protocol server that gives coding agents (Claude Code, Cursor, Cline) grounded access to indicators, error codes, the C-API surface, and full-text doc search:

pip install flox-mcp
flox-mcp init           # writes ./.mcp.json for the current project

See the flox-mcp README for the tool list.

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
    def on_book_update(self, ctx): ...
    def on_bar(self, ctx, bar): ...

    # Order-event hooks (fires on the strategy's own emitted orders).
    def on_fill(self, ctx, ev): ...           # status PARTIALLY_FILLED or FILLED
    def on_order_update(self, ctx, ev): ...   # every status change including fills

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)

OrderEvent (passed to on_fill / on_order_update)

Property	Type	Description
order_id	int	Engine order ID
status	str	"FILLED" / "PARTIALLY_FILLED" / "REJECTED" / "CANCELED" / ...
side	str	"buy" or "sell"
fill_qty	float	Last-fill quantity
fill_price	float	Last-fill price
reject_reason	str | None	Set when status == REJECTED

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 = bt.run_tape("./tape")              # replay a recorded `.floxlog` directory

equity = bt.equity_curve()   # numpy arrays: timestamp_ns, equity, drawdown_pct
trades = bt.trades()         # numpy arrays: per-trade detail

from flox_py.report import write_html
write_html("report.html", stats=stats, equity_curve=equity, trades=trades)

The same risk-gate stack as the live Runner plugs into the backtest. Reduce-only / flatten orders bypass the gate by design (so a tightening cap cannot strand a position):

bt.set_risk_manager(my_risk_manager)        # IRiskManager: .allow(order)
bt.set_kill_switch(my_kill_switch)          # IKillSwitch: .check(order), .is_triggered()
bt.set_order_validator(my_validator)        # IOrderValidator: .validate(order, reason)
bt.set_pnl_tracker(my_pnl_tracker)          # IPnLTracker: .on_order_filled(order)

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 (%)

Walk-forward and grid search

wf = flox.WalkForwardRunner(
    registry, fee_rate=0.0004, initial_capital=10_000,
    mode="anchored", train_size=180, test_size=30,
)
wf.set_strategy_factory(lambda fold_idx: MyStrategy([btc]))
folds = wf.run_csv("data.csv", "BTCUSDT")

grid = flox.GridSearch()
grid.add_axis([5, 10, 20])    # fast period
grid.add_axis([30, 50, 100])  # slow period
def factory(params):
    fast, slow = params
    bt = flox.BacktestRunner(registry, fee_rate=0.0004, initial_capital=10_000)
    bt.set_strategy(MyStrategy([btc]))  # configure with fast / slow as needed
    return bt.run_csv("data.csv", "BTCUSDT")
grid.set_factory(factory)
results = grid.run()    # list of {index, params, stats}

Render a heatmap with flox_py.report.heatmap_html(...) / write_heatmap(...). Run a multiple-comparison-aware significance test with flox.whites_reality_check(returns, num_bootstrap=10_000).

MLflow

from flox_py import mlflow as flox_mlflow

flox_mlflow.log_backtest(
    stats, equity_curve=equity, trades=trades,
    params={"fast": 10, "slow": 30},
    run_name="sma-2025-01",
)

mlflow is optional — install with pip install mlflow.

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, Correlation, AutoCorrelation, 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.