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David Brazda
2024-10-04 12:14:29 +02:00
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research/data/prepare_aggregated_data.ipynb
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"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Loading trades and vectorized aggregation\n", "# Create aggregated data from trades\n",
"Describes how to fetch trades (remote/cached) and use new vectorized aggregation to aggregate bars of given type (time, volume, dollar) and resolution\n",
"\n", "\n",
"`fetch_trades_parallel` enables to fetch trades of given symbol and interval, also can filter conditions and minimum size. return `trades_df`\n", "This is how new aggregated data are created and stored to cache, where can they be loaded. It is created for given symbol, interval and aggregation type/resolution. For example OHLCV_1m, or OHLCV_VOLUME_2000 (volume bars with resolution 2000).\n",
"`aggregate_trades` acceptss `trades_df` and ressolution and type of bars (VOLUME, TIME, DOLLAR) and return aggregated ohlcv dataframe `ohlcv_df`" "\n",
"Possible aggregation types\n",
"- time based OHLCV, time resolution\n",
"- volume based OHLCV, volume resolution\n",
"- dollar based OHLCV, dollar amount resolution\n",
"- renko bars, bricks size as resolution\n",
"\n",
"\n",
"Steps include\n",
"- fetch trades (remote/cached)\n",
"- use new vectorized aggregation to aggregate bars of given type (time, volume, dollar) and resolution\n",
"- store to agg cache\n",
"\n",
"Methods:\n",
"- `fetch_trades_parallel` enables to fetch trades of given symbol and interval, also can filter conditions and minimum size. Returns `trades_df`\n",
"- `aggregate_trades` accepts `trades_df` and resolution and type of bars (VOLUME, TIME, DOLLAR) and return aggregated ohlcv dataframe `ohlcv_df`\n",
"\n",
"TBD will be soon introduced in separate package responsible for fetching the data (cache mngmt, remote fetching and vectorized aggregation) - see (issue)[https://github.com/drew2323/v2trading/issues/250]"
] ]
}, },
{ {

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research/lw_chart_markers.ipynb
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research/robustness/eval_robustness.ipynb
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