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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import MetaTrader5 as mt5\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# --- ACCOUNT DETAILS ---\n",
    "login = 1234567\n",
    "password = \"123456\"\n",
    "server = \"123443556\"\n",
    "\n",
    "# --- INITIALIZE MT5 ---\n",
    "if not mt5.initialize():\n",
    "    print(\" MT5 initialization failed\")\n",
    "    quit()\n",
    "\n",
    "# --- LOGIN ---\n",
    "if not mt5.login(login, password=password, server=server):\n",
    "    print(\" Login failed:\", mt5.last_error())\n",
    "    mt5.shutdown()\n",
    "    quit()\n",
    "print(\" Logged in successfully\")\n",
    "\n",
    "# --- HISTORY RANGE (last 30 days) ---\n",
    "from_date = datetime.now() - timedelta(days=30)\n",
    "to_date = datetime.now()\n",
    "\n",
    "# --- GET CLOSED DEALS ---\n",
    "deals = mt5.history_deals_get(from_date, to_date)\n",
    "if deals is None:\n",
    "    print(\" No deals found:\", mt5.last_error())\n",
    "    mt5.shutdown()\n",
    "    quit()\n",
    "\n",
    "# --- BUILD DATAFRAME ---\n",
    "data = []\n",
    "for d in deals:\n",
    "    order_info = mt5.history_orders_get(ticket=d.order)\n",
    "    sl = tp = None\n",
    "    if order_info and len(order_info) > 0:\n",
    "        sl = order_info[0].sl\n",
    "        tp = order_info[0].tp\n",
    "\n",
    "    deal_type = \"BUY\" if d.type == 0 else \"SELL\" if d.type == 1 else str(d.type)\n",
    "\n",
    "    data.append({\n",
    "        \"Symbol\": d.symbol,\n",
    "        \"Type\": deal_type,\n",
    "        \"Volume, lot\": d.volume,\n",
    "        \"Open price\": d.price if d.entry == 0 else None,\n",
    "        \"Close price\": d.price if d.entry == 1 else None,\n",
    "        \"T/P\": tp,\n",
    "        \"S/L\": sl,\n",
    "        \"Position\": d.position_id,\n",
    "        \"Open time\": datetime.fromtimestamp(d.time_msc/1000) if d.time_msc else datetime.fromtimestamp(d.time),\n",
    "        \"Close time\": datetime.fromtimestamp(d.time_msc/1000) if d.time_msc else datetime.fromtimestamp(d.time),\n",
    "        \"Swap, USD\": d.swap,\n",
    "        \"Reason\": d.reason,\n",
    "        \"P/L, USD\": d.profit\n",
    "    })\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# --- SORT BY CLOSE TIME ---\n",
    "df = df.sort_values(\"Close time\").reset_index(drop=True)\n",
    "\n",
    "# --- CALCULATE EQUITY CURVE ---\n",
    "df[\"Equity\"] = df[\"P/L, USD\"].cumsum()\n",
    "\n",
    "# --- PLOT EQUITY CURVE ---\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=df[\"Close time\"],\n",
    "    y=df[\"Equity\"],\n",
    "    mode=\"lines+markers\",\n",
    "    line=dict(color=\"blue\"),\n",
    "    name=\"Equity Curve\"\n",
    "))\n",
    "fig.update_layout(\n",
    "    title=\"Equity Curve Over Time\",\n",
    "    xaxis_title=\"Close Time\",\n",
    "    yaxis_title=\"Equity\"\n",
    ")\n",
    "fig.show()\n",
    "\n",
    "# --- SHUTDOWN MT5 ---\n",
    "mt5.shutdown()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce58628e-6d26-4f69-a59d-9f249ca5ad15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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