RSI_EMA_Scalping_5min.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/ProAek11/StrategyTradingFrendersis-AI-System/blob/main/RSI_EMA_Scalping_5min.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c1fb55a",
      "metadata": {
        "id": "0c1fb55a"
      },
      "source": [
        "# 5min RSI Scalping Strategy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a0b56a9",
      "metadata": {
        "id": "0a0b56a9",
        "outputId": "76e9c0db-b0ec-4bd7-b875-034bfd9654dd"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Local time</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.02.2019 00:00:00.000 GMT+0200</td>\n",
              "      <td>1.12763</td>\n",
              "      <td>1.12796</td>\n",
              "      <td>1.12762</td>\n",
              "      <td>1.12780</td>\n",
              "      <td>53.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.02.2019 00:05:00.000 GMT+0200</td>\n",
              "      <td>1.12780</td>\n",
              "      <td>1.12785</td>\n",
              "      <td>1.12771</td>\n",
              "      <td>1.12783</td>\n",
              "      <td>36.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.02.2019 00:10:00.000 GMT+0200</td>\n",
              "      <td>1.12782</td>\n",
              "      <td>1.12790</td>\n",
              "      <td>1.12782</td>\n",
              "      <td>1.12787</td>\n",
              "      <td>29.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.02.2019 00:15:00.000 GMT+0200</td>\n",
              "      <td>1.12786</td>\n",
              "      <td>1.12788</td>\n",
              "      <td>1.12778</td>\n",
              "      <td>1.12779</td>\n",
              "      <td>81.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.02.2019 00:20:00.000 GMT+0200</td>\n",
              "      <td>1.12779</td>\n",
              "      <td>1.12787</td>\n",
              "      <td>1.12779</td>\n",
              "      <td>1.12786</td>\n",
              "      <td>91.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Local time     Open     High      Low    Close  \\\n",
              "0  12.02.2019 00:00:00.000 GMT+0200  1.12763  1.12796  1.12762  1.12780   \n",
              "1  12.02.2019 00:05:00.000 GMT+0200  1.12780  1.12785  1.12771  1.12783   \n",
              "2  12.02.2019 00:10:00.000 GMT+0200  1.12782  1.12790  1.12782  1.12787   \n",
              "3  12.02.2019 00:15:00.000 GMT+0200  1.12786  1.12788  1.12778  1.12779   \n",
              "4  12.02.2019 00:20:00.000 GMT+0200  1.12779  1.12787  1.12779  1.12786   \n",
              "\n",
              "   Volume  \n",
              "0   53.71  \n",
              "1   36.31  \n",
              "2   29.00  \n",
              "3   81.81  \n",
              "4   91.52  "
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"EURUSD_Candlestick_5_M_ASK_12.02.2019-12.02.2022.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d727fe79",
      "metadata": {
        "id": "d727fe79",
        "outputId": "fe1413a4-1f7a-4864-bc47-7f9caee8eba7"
      },
      "outputs": [
        {
          "data": {
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Local time</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>224542</th>\n",
              "      <td>11.02.2022 23:35:00.000 GMT+0200</td>\n",
              "      <td>1.13461</td>\n",
              "      <td>1.13503</td>\n",
              "      <td>1.13459</td>\n",
              "      <td>1.13497</td>\n",
              "      <td>192.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224543</th>\n",
              "      <td>11.02.2022 23:40:00.000 GMT+0200</td>\n",
              "      <td>1.13497</td>\n",
              "      <td>1.13498</td>\n",
              "      <td>1.13457</td>\n",
              "      <td>1.13472</td>\n",
              "      <td>126.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224544</th>\n",
              "      <td>11.02.2022 23:45:00.000 GMT+0200</td>\n",
              "      <td>1.13472</td>\n",
              "      <td>1.13472</td>\n",
              "      <td>1.13452</td>\n",
              "      <td>1.13460</td>\n",
              "      <td>130.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224545</th>\n",
              "      <td>11.02.2022 23:50:00.000 GMT+0200</td>\n",
              "      <td>1.13458</td>\n",
              "      <td>1.13495</td>\n",
              "      <td>1.13457</td>\n",
              "      <td>1.13487</td>\n",
              "      <td>278.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224546</th>\n",
              "      <td>11.02.2022 23:55:00.000 GMT+0200</td>\n",
              "      <td>1.13485</td>\n",
              "      <td>1.13533</td>\n",
              "      <td>1.13470</td>\n",
              "      <td>1.13507</td>\n",
              "      <td>621.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Local time     Open     High      Low    Close  \\\n",
              "224542  11.02.2022 23:35:00.000 GMT+0200  1.13461  1.13503  1.13459  1.13497   \n",
              "224543  11.02.2022 23:40:00.000 GMT+0200  1.13497  1.13498  1.13457  1.13472   \n",
              "224544  11.02.2022 23:45:00.000 GMT+0200  1.13472  1.13472  1.13452  1.13460   \n",
              "224545  11.02.2022 23:50:00.000 GMT+0200  1.13458  1.13495  1.13457  1.13487   \n",
              "224546  11.02.2022 23:55:00.000 GMT+0200  1.13485  1.13533  1.13470  1.13507   \n",
              "\n",
              "        Volume  \n",
              "224542  192.66  \n",
              "224543  126.31  \n",
              "224544  130.50  \n",
              "224545  278.16  \n",
              "224546  621.25  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=df[df['Volume']!=0]\n",
        "df.isna().sum()\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "78538592",
      "metadata": {
        "id": "78538592"
      },
      "outputs": [],
      "source": [
        "import pandas_ta as ta\n",
        "df[\"EMA\"] = ta.ema(df.Close, length=50)\n",
        "df[\"RSI\"] = ta.rsi(df.Close, length=3)\n",
        "a=ta.adx(df.High, df.Low, df.Close, length=5)\n",
        "df['ADX']=a['ADX_5']\n",
        "df['ATR']= df.ta.atr()\n",
        "#help(ta.adx)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7096ce0d",
      "metadata": {
        "id": "7096ce0d"
      },
      "source": [
        "# EMA Trend Signal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eead3e73",
      "metadata": {
        "id": "eead3e73"
      },
      "outputs": [],
      "source": [
        "emasignal = [0]*len(df)\n",
        "backcandles = 8\n",
        "\n",
        "for row in range(backcandles, len(df)):\n",
        "    upt = 1\n",
        "    dnt = 1\n",
        "    for i in range(row-backcandles, row+1):\n",
        "        if df.High[i]>=df.EMA[i]:\n",
        "            dnt=0\n",
        "        if df.Low[i]<=df.EMA[i]:\n",
        "            upt=0\n",
        "    if upt==1 and dnt==1:\n",
        "        #print(\"!!!!! check trend loop !!!!\")\n",
        "        emasignal[row]=3\n",
        "    elif upt==1:\n",
        "        emasignal[row]=2\n",
        "    elif dnt==1:\n",
        "        emasignal[row]=1\n",
        "\n",
        "df['EMAsignal'] = emasignal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fbfdf8fb",
      "metadata": {
        "id": "fbfdf8fb"
      },
      "source": [
        "# RSI & ADX Signal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f087b240",
      "metadata": {
        "id": "f087b240"
      },
      "outputs": [],
      "source": [
        "RSIADXSignal = [0] * len(df)\n",
        "for row in range(0, len(df)):\n",
        "    RSIADXSignal[row] = 0\n",
        "    if df.EMAsignal[row]==1 and df.RSI[row]>=80 and df.ADX[row]>=30:\n",
        "        RSIADXSignal[row]=1\n",
        "    if df.EMAsignal[row]==2 and df.RSI[row]<=20 and df.ADX[row]>=30:\n",
        "        RSIADXSignal[row]=2\n",
        "\n",
        "df['RSIADXSignal']=RSIADXSignal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b46ae53",
      "metadata": {
        "id": "5b46ae53"
      },
      "source": [
        "# Candles Signal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "782b919c",
      "metadata": {
        "id": "782b919c"
      },
      "outputs": [],
      "source": [
        "CandleSignal = [0] * len(df)\n",
        "for row in range(1, len(df)):\n",
        "    CandleSignal[row] = 0\n",
        "    #if (RSIADXSignal[row]==1 or RSIADXSignal[row-1]==1) and (df.Open[row]>df.Close[row]):# and df.Close[row]<df.Close[row-1]):\n",
        "    #    CandleSignal[row]=1\n",
        "    #if (RSIADXSignal[row]==2 or RSIADXSignal[row-1]==2) and (df.Open[row]<df.Close[row]):# and df.Close[row]>df.Close[row-1]):\n",
        "    #    CandleSignal[row]=2\n",
        "    if RSIADXSignal[row-1]==1 and df.Open[row]>df.Close[row] and df.Close[row]<min(df.Close[row-1], df.Open[row-1]):\n",
        "        CandleSignal[row]=1\n",
        "    if RSIADXSignal[row-1]==2 and df.Open[row]<df.Close[row] and df.Close[row]>max(df.Close[row-1], df.Open[row-1]):\n",
        "        CandleSignal[row]=2\n",
        "\n",
        "df['TotSignal']=CandleSignal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f2ba461",
      "metadata": {
        "id": "1f2ba461"
      },
      "source": [
        "# Visualizing Signals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46f04543",
      "metadata": {
        "id": "46f04543"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def pointpos(x):\n",
        "    if x['TotSignal']==1:\n",
        "        return x['High']+1e-4\n",
        "    elif x['TotSignal']==2:\n",
        "        return x['Low']-1e-4\n",
        "    else:\n",
        "        return np.nan\n",
        "\n",
        "df['pointpos'] = df.apply(lambda row: pointpos(row), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "408e0f33",
      "metadata": {
        "id": "408e0f33",
        "outputId": "0a3a1faf-5ca2-45ce-9687-5eff3e39a643"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'df' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32mC:\\Users\\Public\\Documents\\Wondershare\\CreatorTemp/ipykernel_15132/3711748576.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdfpl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m fig = go.Figure(data=[go.Candlestick(x=dfpl.index,\n",
            "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "from datetime import datetime\n",
        "\n",
        "dfpl = df[3000:4000]\n",
        "\n",
        "fig = go.Figure(data=[go.Candlestick(x=dfpl.index,\n",
        "                open=dfpl['Open'],\n",
        "                high=dfpl['High'],\n",
        "                low=dfpl['Low'],\n",
        "                close=dfpl['Close']),\n",
        "                go.Scatter(x=dfpl.index, y=dfpl.EMA, line=dict(color='red', width=1), name=\"EMA\")])\n",
        "\n",
        "fig.add_scatter(x=dfpl.index, y=dfpl['pointpos'], mode=\"markers\",\n",
        "                marker=dict(size=5, color=\"MediumPurple\"),\n",
        "                name=\"Signal\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92004262",
      "metadata": {
        "id": "92004262",
        "outputId": "1fe6c40b-4f69-4451-95a5-aadcee3a841e"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Local time</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>EMA</th>\n",
              "      <th>RSI</th>\n",
              "      <th>ADX</th>\n",
              "      <th>ATR</th>\n",
              "      <th>EMAsignal</th>\n",
              "      <th>RSIADXSignal</th>\n",
              "      <th>TotSignal</th>\n",
              "      <th>pointpos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3030</th>\n",
              "      <td>26.02.2019 12:30:00.000 GMT+0200</td>\n",
              "      <td>1.13646</td>\n",
              "      <td>1.13660</td>\n",
              "      <td>1.13629</td>\n",
              "      <td>1.13629</td>\n",
              "      <td>1731.47</td>\n",
              "      <td>1.135933</td>\n",
              "      <td>20.650543</td>\n",
              "      <td>52.088617</td>\n",
              "      <td>0.000318</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3031</th>\n",
              "      <td>26.02.2019 12:35:00.000 GMT+0200</td>\n",
              "      <td>1.13630</td>\n",
              "      <td>1.13639</td>\n",
              "      <td>1.13623</td>\n",
              "      <td>1.13624</td>\n",
              "      <td>1370.44</td>\n",
              "      <td>1.135945</td>\n",
              "      <td>17.305228</td>\n",
              "      <td>41.870703</td>\n",
              "      <td>0.000307</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3032</th>\n",
              "      <td>26.02.2019 12:40:00.000 GMT+0200</td>\n",
              "      <td>1.13626</td>\n",
              "      <td>1.13641</td>\n",
              "      <td>1.13623</td>\n",
              "      <td>1.13634</td>\n",
              "      <td>1075.85</td>\n",
              "      <td>1.135960</td>\n",
              "      <td>44.350358</td>\n",
              "      <td>34.082804</td>\n",
              "      <td>0.000298</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.13613</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "                            Local time     Open     High      Low    Close  \\\n",
              "3030  26.02.2019 12:30:00.000 GMT+0200  1.13646  1.13660  1.13629  1.13629   \n",
              "3031  26.02.2019 12:35:00.000 GMT+0200  1.13630  1.13639  1.13623  1.13624   \n",
              "3032  26.02.2019 12:40:00.000 GMT+0200  1.13626  1.13641  1.13623  1.13634   \n",
              "\n",
              "       Volume       EMA        RSI        ADX       ATR  EMAsignal  \\\n",
              "3030  1731.47  1.135933  20.650543  52.088617  0.000318          2   \n",
              "3031  1370.44  1.135945  17.305228  41.870703  0.000307          2   \n",
              "3032  1075.85  1.135960  44.350358  34.082804  0.000298          2   \n",
              "\n",
              "      RSIADXSignal  TotSignal  pointpos  \n",
              "3030             0          0       NaN  \n",
              "3031             2          0       NaN  \n",
              "3032             0          2   1.13613  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[3030:3033]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5cf58df3",
      "metadata": {
        "id": "5cf58df3"
      },
      "source": [
        "## Stop Loss from Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf9acb37",
      "metadata": {
        "id": "cf9acb37"
      },
      "outputs": [],
      "source": [
        "SLSignal = [0] * len(df)\n",
        "SLbackcandles = 3\n",
        "for row in range(SLbackcandles, len(df)):\n",
        "    mi=1e10\n",
        "    ma=-1e10\n",
        "    if df.TotSignal[row]==1:\n",
        "        for i in range(row-SLbackcandles, row+1):\n",
        "            ma = max(ma,df.High[i])\n",
        "        SLSignal[row]=ma\n",
        "    if df.TotSignal[row]==2:\n",
        "        for i in range(row-SLbackcandles, row+1):\n",
        "            mi = min(mi,df.Low[i])\n",
        "        SLSignal[row]=mi\n",
        "\n",
        "df['SLSignal']=SLSignal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0d2ca08",
      "metadata": {
        "id": "d0d2ca08"
      },
      "source": [
        "## Backtesting Strategy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "661040bd",
      "metadata": {
        "id": "661040bd"
      },
      "outputs": [],
      "source": [
        "dfpl = df[:]\n",
        "def SIGNAL():\n",
        "    return dfpl.TotSignal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d77e20e3",
      "metadata": {
        "id": "d77e20e3",
        "outputId": "0365d0da-9e6f-4a10-addd-6264467169f8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\ProgramFiles\\Anaconda3\\lib\\site-packages\\backtesting\\_plotting.py:47: UserWarning:\n",
            "\n",
            "Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.\n",
            "\n"
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              "    </div>"
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            "application/vnd.bokehjs_load.v0+json": ""
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\ProgramFiles\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning:\n",
            "\n",
            "Data index is not datetime. Assuming simple periods, but `pd.DateTimeIndex` is advised.\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Start                                     0.0\n",
              "End                                  224546.0\n",
              "Duration                             224546.0\n",
              "Exposure Time [%]                    4.231185\n",
              "Equity Final [$]                    97.686878\n",
              "Equity Peak [$]                    100.739541\n",
              "Return [%]                          -2.313122\n",
              "Buy & Hold Return [%]                0.644618\n",
              "Return (Ann.) [%]                         0.0\n",
              "Volatility (Ann.) [%]                     NaN\n",
              "Sharpe Ratio                              NaN\n",
              "Sortino Ratio                             NaN\n",
              "Calmar Ratio                              0.0\n",
              "Max. Drawdown [%]                    -3.22087\n",
              "Avg. Drawdown [%]                   -0.137421\n",
              "Max. Drawdown Duration               187942.0\n",
              "Avg. Drawdown Duration            5994.324324\n",
              "# Trades                               1170.0\n",
              "Win Rate [%]                        40.940171\n",
              "Best Trade [%]                       0.259678\n",
              "Worst Trade [%]                      -0.23257\n",
              "Avg. Trade [%]                      -0.002018\n",
              "Max. Trade Duration                     181.0\n",
              "Avg. Trade Duration                  7.120513\n",
              "Profit Factor                         0.91249\n",
              "Expectancy [%]                      -0.002005\n",
              "SQN                                 -1.328259\n",
              "_strategy                             MyStrat\n",
              "_equity_curve                         Equi...\n",
              "_trades                         Size  Entr...\n",
              "dtype: object"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from backtesting import Strategy\n",
        "from backtesting import Backtest\n",
        "class MyStrat(Strategy):\n",
        "    initsize = 0.02\n",
        "    mysize = initsize\n",
        "    def init(self):\n",
        "        super().init()\n",
        "        self.signal1 = self.I(SIGNAL)\n",
        "\n",
        "    def next(self):\n",
        "        super().next()\n",
        "        slatr = 1.3*self.data.ATR[-1]\n",
        "        TPSLRatio = 1.3\n",
        "\n",
        "        if self.signal1==2 and len(self.trades)==0:\n",
        "            sl1 = self.data.Close[-1] - slatr\n",
        "            tp1 = self.data.Close[-1] + slatr*TPSLRatio\n",
        "            self.buy(sl=sl1, tp=tp1, size=self.mysize)\n",
        "\n",
        "        elif self.signal1==1 and len(self.trades)==0:\n",
        "            sl1 = self.data.Close[-1] + slatr\n",
        "            tp1 = self.data.Close[-1] - slatr*TPSLRatio\n",
        "            self.sell(sl=sl1, tp=tp1, size=self.mysize)\n",
        "\n",
        "bt = Backtest(dfpl, MyStrat, cash=100, margin=1/50, commission=.00)\n",
        "stat = bt.run()\n",
        "stat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a657bdb6",
      "metadata": {
        "id": "a657bdb6"
      },
      "outputs": [],
      "source": [
        "bt.plot(show_legend=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a1d31cf",
      "metadata": {
        "id": "3a1d31cf",
        "outputId": "06785ac2-6a61-4272-fe49-f6721b0c885d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\ProgramFiles\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: UserWarning:\n",
            "\n",
            "Data index is not datetime. Assuming simple periods, but `pd.DateTimeIndex` is advised.\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Start                                     0.0\n",
              "End                                  224546.0\n",
              "Duration                             224546.0\n",
              "Exposure Time [%]                    2.395044\n",
              "Equity Final [$]                     97.01786\n",
              "Equity Peak [$]                      100.1234\n",
              "Return [%]                           -2.98214\n",
              "Buy & Hold Return [%]                0.644618\n",
              "Return (Ann.) [%]                         0.0\n",
              "Volatility (Ann.) [%]                     NaN\n",
              "Sharpe Ratio                              NaN\n",
              "Sortino Ratio                             NaN\n",
              "Calmar Ratio                              0.0\n",
              "Max. Drawdown [%]                   -3.450852\n",
              "Avg. Drawdown [%]                   -1.237493\n",
              "Max. Drawdown Duration               216785.0\n",
              "Avg. Drawdown Duration           74551.666667\n",
              "# Trades                               1214.0\n",
              "Win Rate [%]                        46.540362\n",
              "Best Trade [%]                       0.164641\n",
              "Worst Trade [%]                     -0.188669\n",
              "Avg. Trade [%]                      -0.002513\n",
              "Max. Trade Duration                      59.0\n",
              "Avg. Trade Duration                  3.429984\n",
              "Profit Factor                        0.848276\n",
              "Expectancy [%]                      -0.002506\n",
              "SQN                                  -2.32018\n",
              "_strategy                             MyStrat\n",
              "_equity_curve                        Equit...\n",
              "_trades                         Size  Entr...\n",
              "dtype: object"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfpl = df[:]\n",
        "from backtesting import Strategy\n",
        "from backtesting import Backtest\n",
        "\n",
        "class MyStrat(Strategy):\n",
        "    initsize = 0.02\n",
        "    mysize = initsize\n",
        "    def init(self):\n",
        "        super().init()\n",
        "        self.signal1 = self.I(SIGNAL)\n",
        "\n",
        "    def next(self):\n",
        "        super().next()\n",
        "        TPSLRatio = 1\n",
        "\n",
        "        if self.signal1==2 and len(self.trades)==0:\n",
        "            sl1 = self.data.SLSignal[-1]\n",
        "            tp1 = self.data.Close[-1]+(self.data.Close[-1] - sl1)*TPSLRatio\n",
        "            self.buy(sl=sl1, tp=tp1, size=self.mysize)\n",
        "\n",
        "        elif self.signal1==1 and len(self.trades)==0:\n",
        "            sl1 = self.data.SLSignal[-1]\n",
        "            tp1 = self.data.Close[-1]-(sl1 - self.data.Close[-1])*TPSLRatio\n",
        "            self.sell(sl=sl1, tp=tp1, size=self.mysize)\n",
        "\n",
        "bt = Backtest(dfpl, MyStrat, cash=100, margin=1/50, commission=.00)\n",
        "stat = bt.run()\n",
        "stat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "465554ee",
      "metadata": {
        "id": "465554ee"
      },
      "outputs": [],
      "source": [
        "#bt.plot(show_legend=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "717983c9",
      "metadata": {
        "id": "717983c9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
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      "display_name": "Python 3 (ipykernel)",
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      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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        "version": 3
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      "file_extension": ".py",
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}