Rebalancing-Backtest.ipynb

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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Define percentages I want in floats\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from pandas.tseries.offsets import *\n",
    "\n",
    "df = pd.read_csv('/price-history.csv', index_col=0)\n",
    "df.index = pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_n_random_assets(n, df): \n",
    "    return pd.concat([df.sample(n=n-1, axis=1), btc_df], axis=1)\n",
    "\n",
    "\n",
    "def gen_rebalancing_dates(date, end_date, rebalancing_period):\n",
    "    rebalancing_periods = []\n",
    "    while date < end_date:\n",
    "        date = date + rebalancing_period\n",
    "        rebalancing_periods.append(date)\n",
    "    return rebalancing_periods\n",
    "\n",
    "def annualize_returns(series, years):\n",
    "    return (((series[-1]/ series[0])) ** (1/years))-1\n",
    "\n",
    "\n",
    "def set_initial_allocation(tickers):\n",
    "    allocation = {}\n",
    "    for ticker in tickers:\n",
    "        allocation[ticker] = 1/len(tickers)\n",
    "    return pd.Series(allocation)\n",
    "\n",
    "def set_min_trading_sizes(tickers, min_btc_amount):\n",
    "    trading_limits_btc = {}\n",
    "    for ticker in tickers:\n",
    "        trading_limits_btc[ticker] = min_btc_amount\n",
    "    return pd.Series(trading_limits_btc)\n",
    "    \n",
    "\n",
    "def calc_returns(df, period ):\n",
    "    returns = {}\n",
    "    tickers = df.columns \n",
    "    for ticker in tickers:\n",
    "        returns[ticker] = annualize_returns(df[ticker], period)\n",
    "    return returns\n",
    "\n",
    "def calc_max_return_difference(df, years):\n",
    "    max_return = 0\n",
    "    for column in df:\n",
    "        returns = annualize_returns(df[column], years)\n",
    "        if returns > max_return:\n",
    "            max_return = returns\n",
    "    return max_return\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebalance (df, rebalancing_period, increment_period, starting_amount):\n",
    "    \n",
    "    starting_date = df.index[0]\n",
    "    output = {'date': [starting_date], 'value': [starting_amount], 'fees': [0], 'failed transactions':[0], 'successful transactions': [0]}\n",
    "    date = df.index[1]\n",
    "    end_date = df.index[-1]\n",
    "    years = (end_date - starting_date).days / 365\n",
    "    \n",
    "    tickers = df.columns\n",
    "    tick_size = set_min_trading_sizes(tickers, 0.001)\n",
    "    target_allocation = set_initial_allocation(tickers)\n",
    "    \n",
    "    amount = starting_amount\n",
    "    allocation = target_allocation * amount\n",
    "    rebalancing_array = gen_rebalancing_dates(starting_date, end_date, rebalancing_period)\n",
    "    \n",
    "    no_rebalance_end_value = ((df.loc[end_date] / df.loc[date]) * allocation).sum()\n",
    "    drifting_return = (((no_rebalance_end_value / amount)) ** (1/years)) - 1\n",
    "    \n",
    "    while date < end_date:\n",
    "        # if it is a rebalance date\n",
    "        if date in rebalancing_array:\n",
    "            previous_price = df.loc[date - increment_period]\n",
    "            current_price = df.loc[date]\n",
    "            current_alloc = ((current_price / previous_price) * allocation)\n",
    "            new_alloc = current_alloc.sum() * target_allocation\n",
    "            trading_limits_usd = tick_size * df.loc[date]['bitcoin']\n",
    "            alloc_diff = current_alloc - new_alloc\n",
    "            coins_to_sell = alloc_diff[alloc_diff > 0]\n",
    "            coins_to_sell_minus_btc = coins_to_sell[coins_to_sell.index !='bitcoin']\n",
    "            increments_to_sell = coins_to_sell_minus_btc // trading_limits_usd\n",
    "            failed_sells = np.count_nonzero(increments_to_sell < 1 & pd.notna(increments_to_sell))\n",
    "            successful_sells = np.count_nonzero([increments_to_sell > 1])\n",
    "            amounts_to_sell =  increments_to_sell * trading_limits_usd\n",
    "            sell_alloc = amounts_to_sell.fillna(0)\n",
    "            intermittent_alloc = current_alloc - sell_alloc\n",
    "            sell_fees = sell_alloc.sum() * 0.001 #fee %\n",
    "            intermittent_alloc['bitcoin'] = intermittent_alloc['bitcoin'] + sell_alloc.sum() - sell_fees\n",
    "            new_alloc_diff = new_alloc - intermittent_alloc \n",
    "            coins_to_buy = new_alloc_diff[new_alloc_diff > 0]\n",
    "            coins_to_buy_no_btc = coins_to_buy[coins_to_buy.index !='bitcoin']\n",
    "            increments_to_buy = coins_to_buy_no_btc // trading_limits_usd\n",
    "            successful_buys = np.count_nonzero([increments_to_buy > 1])\n",
    "            failed_buys = np.count_nonzero([increments_to_buy < 1 & pd.notna(increments_to_buy)])\n",
    "            amounts_to_buy = increments_to_buy * trading_limits_usd\n",
    "            buy_alloc = amounts_to_buy.fillna(0)\n",
    "            buy_fees_alloc = buy_alloc * 0.001 #fee %\n",
    "            final_alloc = intermittent_alloc + buy_alloc - buy_fees_alloc\n",
    "            final_alloc['bitcoin'] = final_alloc['bitcoin'] - buy_alloc.sum()\n",
    "            total_fees = buy_fees_alloc.sum() + sell_fees\n",
    "            allocation = final_alloc\n",
    "            failed_trades = failed_sells + failed_buys \n",
    "            successful_trades = successful_sells + successful_buys\n",
    "            output['fees'].append(total_fees)\n",
    "            output['date'].append(date)\n",
    "            output['value'].append(allocation.sum()) \n",
    "            output['failed transactions'].append(failed_trades)\n",
    "            output['successful transactions'].append(successful_trades)\n",
    "            date += increment_period                \n",
    "        # if it is not a rebalance date\n",
    "        else:\n",
    "            previous_price = df.loc[date - increment_period]\n",
    "            current_price = df.loc[date]\n",
    "            allocation = ((current_price / previous_price) * allocation)\n",
    "            amount = allocation.sum()\n",
    "            output['fees'].append(0)\n",
    "            output['date'].append(date)\n",
    "            output['value'].append(amount)\n",
    "            output['failed transactions'].append(0)\n",
    "            output['successful transactions'].append(0)\n",
    "            date += increment_period  \n",
    "    \n",
    "    rebalancing_df = pd.DataFrame(output).set_index('date')\n",
    "    max_return_difference = calc_max_return_difference(df, years)\n",
    "    rebalancing_return = annualize_returns(rebalancing_df['value'], years)\n",
    "    total_fees = rebalancing_df['fees'].sum()\n",
    "    failed_trades = rebalancing_df['failed transactions'].sum()\n",
    "    successful_trades = rebalancing_df['successful transactions'].sum()\n",
    "    successful_trade_percentage = successful_trades / (successful_trades + failed_trades)\n",
    "    return {\n",
    "        'Return Difference': max_return_difference *100,\n",
    "        'Rebalancing Bonus' : (rebalancing_return *100) - (drifting_return *100),\n",
    "        'Rebalanced End Value': rebalancing_df['value'][-1] ,\n",
    "        'Total Fees': total_fees,\n",
    "        'Successful Trading Percentage': successful_trade_percentage,\n",
    "        'Tickers': tickers.values,\n",
    "        \n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rebalancing period   1  num_asset  10 simulation   1\n",
      "rebalancing period   1  num_asset  10 simulation   2\n"
     ]
    }
   ],
   "source": [
    "evaluate_dict = {'Return Difference': [], 'Rebalancing Bonus': [], 'Rebalanced End Value': [], 'Total Fees': [], 'Successful Trading Percentage': [], 'Rebalancing Period': [], 'Tickers': [] }\n",
    "rebalancing_periods = []\n",
    "\n",
    "# Number of simulations\n",
    "for simulation in range(1,3):\n",
    "        random_assets = get_n_random_assets(10, df)\n",
    "        for rebalancing_period in range(1,2):\n",
    "            print('rebalancing period', ' ', rebalancing_period, ' num_asset ', 10, 'simulation', ' ', simulation)\n",
    "            result = rebalance(random_assets, pd.Timedelta(days=rebalancing_period), pd.Timedelta(days=1), 1000)\n",
    "            evaluate_dict['Return Difference'].append(result['Return Difference'])\n",
    "            evaluate_dict['Rebalancing Bonus'].append(result['Rebalancing Bonus'])\n",
    "            evaluate_dict['Rebalanced End Value'].append(result['Rebalanced End Value'])\n",
    "            evaluate_dict['Total Fees'].append(result['Total Fees'])\n",
    "            evaluate_dict['Successful Trading Percentage'].append(result['Successful Trading Percentage'])\n",
    "            evaluate_dict['Rebalancing Period'].append(rebalancing_period)\n",
    "            evaluate_dict['Tickers'].append(str(result['Tickers']))\n",
    "evaluate_df = pd.DataFrame(evaluate_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return Difference</th>\n",
       "      <th>Rebalancing Bonus</th>\n",
       "      <th>Rebalanced End Value</th>\n",
       "      <th>Total Fees</th>\n",
       "      <th>Successful Trading Percentage</th>\n",
       "      <th>Rebalancing Period</th>\n",
       "      <th>Tickers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.2643</td>\n",
       "      <td>71.456036</td>\n",
       "      <td>1400.901757</td>\n",
       "      <td>83.169877</td>\n",
       "      <td>0.555422</td>\n",
       "      <td>1</td>\n",
       "      <td>['revain' 'dent' 'aion' 'enigma' 'enjin-coin' ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.686982</td>\n",
       "      <td>463.785862</td>\n",
       "      <td>12.038766</td>\n",
       "      <td>0.072878</td>\n",
       "      <td>1</td>\n",
       "      <td>['enigma' 'vertcoin' 'zencash' 'monero' 'qtum'...</td>\n",
       "    </tr>\n",
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       "   Return Difference  Rebalancing Bonus  Rebalanced End Value  Total Fees  \\\n",
       "0            31.2643          71.456036           1400.901757   83.169877   \n",
       "1             0.0000          13.686982            463.785862   12.038766   \n",
       "\n",
       "   Successful Trading Percentage  Rebalancing Period  \\\n",
       "0                       0.555422                   1   \n",
       "1                       0.072878                   1   \n",
       "\n",
       "                                             Tickers  \n",
       "0  ['revain' 'dent' 'aion' 'enigma' 'enjin-coin' ...  \n",
       "1  ['enigma' 'vertcoin' 'zencash' 'monero' 'qtum'...  "
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     "execution_count": 13,
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    "evaluate_df"
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