XXSim 0.10.0

Exchange Execution Simulator - stock exchange single bar execution simulator

XXSim

Exchange Execution Simulator, development package. This package simulates order(s) execution based on provided market OHLCV data. This is not an independent package, but its the core logic required to implement backtesting when it comes on running on candlebar data.

This is the first drop and wasn't tested out in the wild yet, more functionality and capabilities to come

disclaimer

Own risk warning - Execution prices are best estimations base on worst case scenarios and statistics, there will be price differences between simulations and real-world execution, using this package the user acknowledges his consent and takes full responsibility on the implications caused due to any error or misinterpetation of this package and it's results.

Trailing commands warning - Trailing commands are currently roughly estimated and carry high deviation from the real world

The challenge

Core Problem: Reconstructing intra-bar price movement to determine order execution.

The golden standard of market data comes in chunks of Candle-bars providing Open, High, Low, Close and Volume of predefined time range ie. 1-minute, 5-minutes, an hour, a day, a week, a month, etc... Within each such data-unit there is a gap of the inner price motions, unless you work with tick-by-tick data which is expensive, noisy and resource intense. On top of that, fill prices are results of a consiquent rules and formations that are hard to simulate.

The solution

Output: Realistic execution fills within statistical uncertainty. or consiquent order either original or modified.

XSim relies on OHLC Data to simulate the inner motion of prices within single data-unit, and attempt to perform a set of decision to execute orders in the most authentic way.

Supported order types

• MarketOrder
• LimitOrder
• StopOrder
• StopLimitOrder
• TrailingStopMarket

Not supported order types (at the moment)

• Trailing Stop Limit Orders
• Market-on-Close (MOC) / Limit-on-Close (LOC)
• Market-if-Touched (MIT)
• Others...

Current Execution algorithm assumptions

• Optional slippage model — volatility-based normal distribution slippage, configurable via ExecutionConfig
  • slippage_model="none" (default): deterministic fills at exact price
  • slippage_model="normal": slippage drawn from N(0, bar_range / std_divider)
  • Direction-aware: slippage magnitude scales with bar direction (adverse = full, favorable = 25%)
  • Trail orders use next price fragment for direction instead of overall bar direction
  • Result always clamped to [bar.low, bar.high]
  • Seeded RNG via random_seed for reproducible backtests
• No partial fills
• Aggressive approach - Order will be filled if there's a possible path between order's formation and the candlebar.
• Trail orders assume the following order of the candles: On Bullish bar: prev_extremePrice [optional] -> open -> low -> high -> close On Bearish bar: prev_extremePrice [optional] -> open -> high -> low -> close

Installation

pip install XXSim

Usage

Single-Bar Execution

For direct single-bar execution using the low-level engine:

from XXSim import ExecutionEngine, MarketOrder, BarData
from datetime import datetime

engine = ExecutionEngine()
bar = BarData(
    date=datetime(2025, 1, 1, 9, 30),
    open=100.00,
    high=105.00,
    low=95.00,
    close=102.00,
    volume=1000000,
)
order = MarketOrder(action='BUY', totalQuantity=100)
fills = engine.execute(order, bar)
print(fills)

Multi-Bar Simulation

For backtesting across multiple bars with order lifecycle management:

from XXSim import Simulator, MarketOrder, LimitOrder, BarData

sim = Simulator()

# Register callbacks
sim.on_fill(lambda trade, fill: print(f"Filled: {fill.execution.price}"))
sim.on_cancel(lambda trade: print(f"Cancelled: {trade.log[-1].message}"))

# Submit orders
sim.submit_order(MarketOrder(action='BUY', totalQuantity=100))
sim.submit_order(LimitOrder(action='SELL', totalQuantity=100, price=110.0))

# Process bars
for bar in historical_bars:
    fills = sim.process_bar(bar)

# Or use ib_insync-style event loop
def strategy(bar, fills):
    if should_buy(bar):
        sim.submit_order(MarketOrder(action='BUY', totalQuantity=100))

sim.on_bar(strategy)
sim.run(historical_bars)

The Simulator supports:

• Order management: submit, cancel, update, query orders
• TIF (Time-In-Force): GTC, DAY (expires on date change), GTD (expires after date)
• GAT (Good After Time): Orders with goodAfterTime are not active until that time
• OCO (One-Cancels-Other): Link orders via ocaGroup - when one fills, siblings are cancelled
• Callbacks: on_fill, on_cancel, on_update, on_bar

Development

Running Tests

pytest tests/ -v

Visualizations

Stop-limit and Trailing test cases can be visualized For stop-limit visualization run:

python docs/stop-limit-chart-generator.py test-data/stop-limit/<filename.csv>

For trailing visualization run:

python docs/trailing-stop-chart-generator.py test-data/trailing-stop/<filename.csv>

TODO: Trailing cases are not organized systemactically enough

Contributing

Contributions are welcome! Please see the documentation in the docs folder for more details and test specifications.