MA_cross_strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moving average crossover Strategy\n",
    "- strategy: `script/strategy.py -> mac()`\n",
    "- generate signals\n",
    "- backtest: `script/backtest.py -> bt()`\n",
    "- evaluation: **Sharpe ratio**,..., etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data_folder = \"./data/\"\n",
    "crypto_df = pd.read_csv(data_folder + 'train.csv')\n",
    "asset_details = pd.read_csv(data_folder + 'asset_details.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trading rule details\n",
    "\n",
    "- Use **BTC** for testing the strategy.\n",
    "- create two separate Simple Moving Averages (SMA) of a time series with differing lookback periods,\n",
    "    let’s say, 40 days and 100 days. \n",
    "- long moving tends toward short moving.\n",
    "- If the short moving average exceeds the long moving average then you go long.\n",
    "- if the long moving average exceeds the short moving average then you exit.\n",
    "- Only long/sell once if consecutive signal occurs.\n",
    "- ignore the transaction fee for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "           timestamp  Asset_ID   Count          Open          High       Low  \\\n",
       "2         1514764860         1   229.0  13835.194000  14013.800000  13666.11   \n",
       "10        1514764920         1   235.0  13835.036000  14052.300000  13680.00   \n",
       "18        1514764980         1   528.0  13823.900000  14000.400000  13601.00   \n",
       "26        1514765040         1   435.0  13802.512000  13999.000000  13576.28   \n",
       "34        1514765100         1   742.0  13766.000000  13955.900000  13554.44   \n",
       "...              ...       ...     ...           ...           ...       ...   \n",
       "24236739  1632182160         1  1940.0  42983.780000  43001.850849  42878.26   \n",
       "24236753  1632182220         1  2026.0  42904.197143  42932.000000  42840.16   \n",
       "24236767  1632182280         1  1986.0  42859.385714  42887.500000  42797.20   \n",
       "24236781  1632182340         1  4047.0  42839.012802  43042.160000  42818.10   \n",
       "24236795  1632182400         1  2698.0  43009.961250  43048.510000  42961.64   \n",
       "\n",
       "                 Close      Volume          VWAP    Target  \n",
       "2         13850.176000   31.550062  13827.062093 -0.014643  \n",
       "10        13828.102000   31.046432  13840.362591 -0.015037  \n",
       "18        13801.314000   55.061820  13806.068014 -0.010309  \n",
       "26        13768.040000   38.780529  13783.598101 -0.008999  \n",
       "34        13724.914000  108.501637  13735.586842 -0.008079  \n",
       "...                ...         ...           ...       ...  \n",
       "24236739  42899.012857   56.850913  42935.489499       NaN  \n",
       "24236753  42860.005714   80.993326  42879.576084       NaN  \n",
       "24236767  42827.020000   65.677734  42844.090693       NaN  \n",
       "24236781  43017.277143  138.335477  42935.761938       NaN  \n",
       "24236795  43002.505000  128.206820  43011.414052       NaN  \n",
       "\n",
       "[1956282 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc = crypto_df[crypto_df[\"Asset_ID\"]== 1 ]\n",
    "btc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate signals for daily BTC\n",
    "\n",
    "- use daily BTC close price as the asset price to trade with.\n",
    "- signal code '1' short moving average exceeds the long moving average, signal code '0' otherwise.\n",
    "- visualize the signals that '^' for \"buying\" and 'v' for \"selling\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dingxian/Documents/GitHub/Crypto_Forecasting_kaggle/script/morestats.py:29: FutureWarning: casting datetime64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
      "  t = date_str.astype('int64') // 10**9 ##unix time with nanoseconds //10**9\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>signal</th>\n",
       "      <th>short_mavg</th>\n",
       "      <th>long_mavg</th>\n",
       "      <th>positions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1514851200</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13432.9180</td>\n",
       "      <td>13432.9180</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514937600</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14089.5150</td>\n",
       "      <td>14089.5150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515024000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14427.0080</td>\n",
       "      <td>14427.0080</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515110400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14608.9460</td>\n",
       "      <td>14608.9460</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515196800</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15077.1328</td>\n",
       "      <td>15077.1328</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            signal  short_mavg   long_mavg  positions\n",
       "1514851200     0.0  13432.9180  13432.9180        NaN\n",
       "1514937600     0.0  14089.5150  14089.5150        0.0\n",
       "1515024000     0.0  14427.0080  14427.0080        0.0\n",
       "1515110400     0.0  14608.9460  14608.9460        0.0\n",
       "1515196800     0.0  15077.1328  15077.1328        0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from script.strategy import mac\n",
    "from script.morestats import ohlcv_with_frequency\n",
    "btc_daily = ohlcv_with_frequency(df = btc,frequency='1D')\n",
    "signals = mac(asset_df=btc_daily,\n",
    "                price_col='Close', short_window=40,long_window=100, plot_signal=True)\n",
    "signals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting on daily BTC\n",
    "- We buy or sell the fixed share once on the first signal among consecutive same signals.\n",
    "- The share to buy or sell every time is fixed as 100 unit(level doesn't matter when fixed).\n",
    "- Initial capital to play is 100000 USD.\n",
    "- Visualize the account balance change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shares</th>\n",
       "      <th>holdings</th>\n",
       "      <th>cash</th>\n",
       "      <th>balance</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1514851200</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514937600</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515024000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515110400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515196800</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Shares  holdings      cash   balance  returns\n",
       "1514851200     0.0       0.0  100000.0  100000.0      NaN\n",
       "1514937600     0.0       0.0  100000.0  100000.0      0.0\n",
       "1515024000     0.0       0.0  100000.0  100000.0      0.0\n",
       "1515110400     0.0       0.0  100000.0  100000.0      0.0\n",
       "1515196800     0.0       0.0  100000.0  100000.0      0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from script.backtest import bt\n",
    "portfolio = bt(asset_df=btc_daily, signals=signals, strategy='mac',\n",
    "                initial_capital = float(100000.0), price_col= 'Close', \n",
    "                shares_signal=100, plot_balance=True)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the strategy\n",
    "- Sharpe ratio: the ratio between the returns and the additional risk that is incurred is quite OK. Usually, a ratio greater than 1 is acceptable by investors, 2 is very good and 3 is excellent. $$S_{a}=\\frac{E\\left[R_{a}-R_{risk free}\\right]}{\\sigma_{a}}$$\n",
    "- Maximum Drawdown: the largest single drop from peak to bottom in the value of a portfolio before a new peak is achieved.\n",
    "- Compound Annual Growth Rate (CAGR): $$(EV / BV)^{1 / n}-1$$\n",
    "- distribution of returns\n",
    "- trade-level metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.512202147383543"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = portfolio['returns']\n",
    "# annualized Sharpe ratio, BTC market opens everyday\n",
    "daily_sharpe =(returns.mean() - 0 )/ returns.std()\n",
    "sharpe_ratio = np.sqrt(365) * daily_sharpe\n",
    "sharpe_ratio"
   ]
  }
 ],
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