Added sklearn model to Jupyter sample

samples/jupyter/README.md

@@ -0,0 +1,9 @@
+# Jupyter Sample
+
+This is sample that will show you how to use the OpenApiPy Python package on a Jupyter notebook.
+
+In the notebook we get the daily bars data from cTrader Open API, then we use it to train a sklearn model.
+
+To use the sample you have to create a copy of "credentials.json" file and rename it to "credentials-dev.json".
+
+Then fill the file with your Open API application credentials, access token, and a trading account ID.

samples/jupyter/main.ipynb

@@ -151,7 +151,7 @@
     "    highPrice = (trendbar.low + trendbar.deltaHigh) / 100000.0\n",
     "    lowPrice = trendbar.low / 100000.0\n",
     "    closePrice = (trendbar.low + trendbar.deltaClose) / 100000.0\n",
-    "    return (openTime, openPrice, highPrice, lowPrice, closePrice, trendbar.volume)"
+    "    return [openTime, openPrice, highPrice, lowPrice, closePrice, trendbar.volume]"
    ]
   },
   {
@@ -195,6 +195,8 @@
     "    dailyBars.clear()\n",
     "    dailyBars.extend(barsData)\n",
     "    print(\"\\ndailyBars length:\", len(dailyBars))\n",
+    "    print(\"\\Stopping reactor...\")\n",
+    "    reactor.stop()\n",
     "    \n",
     "def symbolsResponseCallback(result):\n",
     "    print(\"\\nSymbols received\")\n",
@@ -292,7 +294,7 @@
    "id": "a7835d65",
    "metadata": {},
    "source": [
-    "Now we have the price data, let'd do some cool stuff with it, we will transform it to a pandas DataFrame and then we will use it to train a sklearn model based on it."
+    "Now we have the price data, let's do some cool stuff with it, we will transform it to a pandas DataFrame:"
    ]
   },
   {
@@ -301,7 +303,90 @@
    "id": "5a75f17c",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f6bdcd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(np.array(dailyBars),\n",
+    "                   columns=['Time', 'Open', 'High', 'Low', 'Close', 'Volume'])\n",
+    "df[\"Open\"] = pd.to_numeric(df[\"Open\"])\n",
+    "df[\"High\"] = pd.to_numeric(df[\"High\"])\n",
+    "df[\"Low\"] = pd.to_numeric(df[\"Low\"])\n",
+    "df[\"Close\"] = pd.to_numeric(df[\"Close\"])\n",
+    "df[\"Volume\"] = pd.to_numeric(df[\"Volume\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63eda941",
+   "metadata": {},
+   "source": [
+    "Now let's create a labels series, we will use it later for our ML model.\n",
+    "This will have a 1 if the close price was higher than open price and 0 otherwise.\n",
+    "We will use the today price data to predict the tomorrow bar type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02856070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Labels\"] = (df[\"Close\"] > df[\"Open\"]).astype(int)\n",
+    "df[\"Labels\"] = df[\"Labels\"].shift(-1)\n",
+    "df.drop(df.tail(1).index,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb75ff4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d5ce793",
+   "metadata": {},
+   "source": [
+    "Now let's create a sklearn LogisticRegression model and use our data to train and test it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc9f1fe5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "x = df.loc[:, [\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
+    "y = df.Labels\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7)\n",
+    "\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred= model.predict(x_test)\n",
+    "\n",
+    "print(\"Our Model accuracy score is: \", accuracy_score(y_test, y_pred))"
+   ]
   }
  ],
  "metadata": {