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docs(py): update the examples.
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examples/Level-3 Backtesting.ipynb

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"id": "cf051d07-5757-4609-a46b-b4a48be72749",
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"metadata": {},
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"source": [
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"The impact of the difference can vary depending on the characteristics of the strategy; for some strategies, Level-2 estimation may be sufficiently accurate, while for others, it may not be. This comparison is intended to highlight these differences. In markets that only provide Level-2 data, it is important to develop a realistic queue position model based on live trading data. Although Level-3 data offers direct order queue position information, it is still crucial to validate backtesting results against live trading results. For example, in this CME Level-3 backtest, the fill simulation does not account for order fills by implied orders."
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"The impact of the difference can vary depending on the characteristics of the strategy; for some strategies, Level-2 estimation may be sufficiently accurate, while for others, it may not be. This comparison is intended to highlight these differences. In markets that only provide Level-2 data, it is important to develop a realistic queue position model based on live trading data. Although Level-3 data offers direct order queue position information, it is still crucial to validate backtesting results against live trading results. For example, in this CME Level-3 backtest, the market depth doesn't include implied orders."
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examples/Queue-Based Market Making in Large Tick Size Assets.ipynb

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" max_position = grid_num * order_qty\n",
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" # Our half spread is just half a tick size,\n",
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" # but it's considered a round-off error, so we use 0.49, which is slightly less than 0.5.\n",
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" # If you set a lower value, the order will tend to stay to the best bid and offer, even when book pressure increases.\n",
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" # You can think of it as a threshold for backing off based on book pressure.\n",
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" half_spread = tick_size * 0.49\n",
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" grid_interval = tick_size\n",
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" skew_adj = 1.0\n",

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