daily update

research/data/prepare_aggregated_data.ipynb

@@ -4,11 +4,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Loading trades and vectorized aggregation\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",
+    "# Create aggregated data from trades\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",
-    "`aggregate_trades` acceptss `trades_df` and ressolution and type of bars (VOLUME, TIME, DOLLAR) and return aggregated ohlcv dataframe `ohlcv_df`"
+    "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",
+    "\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]"
    ]
   },
   {