flox-py

Python bindings for FLOX — an AI-native framework for building trading systems.

AI agents discover the surface and drive it end-to-end through an MCP control plane. One strategy class runs backtest, paper, and live.

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_ratio 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.

Project links

Data verified by PyPI on Aug 12, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.7.2)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for eeiaao from gravatar.com eeiaao

Credits

Author: FLOX Foundation

GitHub Statistics

Data verified by PyPI on Aug 12, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

ccxt mlflow mcp

Tags

trading backtest crypto perpetual cross-margin liquidation funding paper-trading live-trading ccxt venue-realistic execution market-making mcp ai-agent indicators technical-analysis walk-forward monte-carlo reproducibility

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