Update example.

notebooks/example_dydx_v3.ipynb

@@ -4,17 +4,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Intro\n",
-    "Running this notebook will create the file `locast.db` in the notebooks folder (if not yet present), containing a price candle cluster for ETH-USD. \n",
+    "## Intro\n",
+    "Running this notebook will create the file `locast.db` in the notebooks folder (if not yet present), containing a price candle cluster for ETH-USD.\n",
+    "Note: All examples share the same `locast.db`. So if one creates it, the other one will use it automatically. \n",
     "Once it exists, re-running this notebook will attempt to update that cluster. Depending on how much time has passed, this might not be necessary yet.\n",
     "\n",
-    "## Cluster\n",
-    "A cluster is a list of candle objects, representing a time series. Per exchange, market and resolution, there can be exactly one cluster in the database. Once it is created, it can either be retrieved, deleted or updated of price data. "
+    "### Cluster\n",
+    "A cluster is a list of candle objects, representing a time series of price data. Per exchange, market and resolution, there can be exactly one cluster in the database. Once it is created, it can either be:\n",
+    "- retrieved \n",
+    "- deleted\n",
+    "- updated\n",
+    "- ... more to come\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -25,7 +30,7 @@
       "🚛 10000 of 10000 candles fetched. ✅\n",
       "🚛 2 of 2 candles fetched. ✅\n",
       "📀 10002 of 10002 candles stored. ✅\n",
-      "✅ Cluster created in 93.85 seconds.\n",
+      "✅ Cluster created in 118.32 seconds.\n",
       "🕯️ETH-USD-Candles in cluster: 10002.\n"
      ]
     }
@@ -128,7 +133,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,