research added (#207)

research/prepare_aggregatied_data.ipynb

@@ -5,7 +5,7 @@
    "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",
+    "This notebook fetches the trades from remote or local cache and aggregates them to bars of given type (time, volume, dollar) and resolution\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`"
@@ -1594,7 +1594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

research/strat_LINREG_MULTI/v1_SINGLE.ipynb

@@ -43885,7 +43885,6 @@
     "\n",
     "# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
     "#                                                                                         line=dict(color=\"yellow\"),                                                                                     \n",
-    "                                                                                        \n",
     "#                                                                                         fill=None,\n",
     "#                                                                                         connectgaps=True,\n",
     "#                                                                                         ))\n",