diff --git a/asset_backtester/__init__.py b/asset_backtester/__init__.py index 0bd50a9..d85d895 100644 --- a/asset_backtester/__init__.py +++ b/asset_backtester/__init__.py @@ -1 +1,3 @@ +from . import datahandler, charts from .backtester import Backtest +from .portfolio import * diff --git a/asset_backtester/backtester.py b/asset_backtester/backtester.py index 1f16e1c..fe7fb65 100644 --- a/asset_backtester/backtester.py +++ b/asset_backtester/backtester.py @@ -1,7 +1,7 @@ import pandas as pd import pyprind -from portfolio import Portfolio +from .portfolio import Portfolio class Backtest: @@ -9,7 +9,6 @@ class Backtest: self.schema = schema self._portfolio = None self._data = None - self.data_symbol = None @property def portfolio(self): @@ -27,9 +26,8 @@ class Backtest: @data.setter def data(self, data): self._data = data - self.data_symbol = df_symbol(data) - def run(self, initial_capital=1_000_000, periods='1', sma_months=None): + def run(self, initial_capital=1_000_000, periods=1, sma_months=None): """Runs a backtest and returns a dataframe with the daily balance""" assert self._data is not None assert self._portfolio is not None @@ -41,9 +39,9 @@ class Backtest: data_iterator = self._data.iter_dates() - first_day = self._data['date'].iloc[0] - last_day = self._data['date'].iloc[-1] - rebalancing_days = pd.date_range(first_day, last_day, freq=periods + + first_day = self._data['date'].min() + last_day = self._data['date'].max() + rebalancing_days = pd.date_range(first_day, last_day, freq=str(periods) + 'BMS').to_pydatetime() if periods is not None else [] bar = pyprind.ProgBar(data_iterator.ngroups, bar_char='█') @@ -55,14 +53,10 @@ class Backtest: index=[self._data.start_date - pd.Timedelta(1, unit='day')]) for date, data in data_iterator: - if date == self._data._data['date'][0]: + if date in rebalancing_days or date == first_day: self._rebalance_portfolio(data) self._update_balance(date, data) - - if date in rebalancing_days: - self._rebalance_portfolio(data) - bar.update() self.balance['% change'] = self.balance['capital'].pct_change() @@ -73,12 +67,15 @@ class Backtest: def _rebalance_portfolio(self, data): """Rebalances the portfolio so that the total money is allocated according to the given percentages""" money_total = self.current_cash + self.current_capital + for asset in self._portfolio.assets: - asset_current = data[data['symbol'] == asset.symbol] - asset_price = asset_current[self.schema['Adj Close']].values[0] + query = '{} == "{}"'.format(self.schema['symbol'], asset.symbol) + + asset_current = data.query(query) + asset_price = asset_current[self.schema['adjClose']].values[0] qty = (money_total * asset.percentage) // asset_price - inventory_entry = self.inventory[self.inventory['symbol'] == asset.symbol] + inventory_entry = self.inventory.query(query) self.inventory.drop(inventory_entry.index, inplace=True) updated_asset = pd.Series([asset.symbol, asset_price, qty]) updated_asset.index = self.inventory.columns @@ -93,10 +90,11 @@ class Backtest: costs = [] for asset in self._portfolio.assets: - asset_current = data[data['symbol'] == asset.symbol] - inventory_asset = self.inventory[self.inventory['symbol'] == asset.symbol] + query = '{} == "{}"'.format(self.schema['symbol'], asset.symbol) + asset_current = data.query(query) + inventory_asset = self.inventory.query(query) - cost = asset_current[self.schema['Adj Close']].values[0] + cost = asset_current[self.schema['adjClose']].values[0] qty = inventory_asset['qty'].values[0] costs.append(cost * qty) @@ -110,29 +108,27 @@ class Backtest: 'capital': money_total, }, name=date) self.balance = self.balance.append(row) + + class df_symbol: + def __init__(self, data, sma_months=None): - def __init__(self, data, sma_months = None): + self.columns = data['symbol'].drop_duplicates(keep='first') - self.columns = data['symbol'].drop_duplicates(keep = 'first') - - cols = pd.MultiIndex.from_product([self.columns.to_list(),data.columns[1:-1].to_list()]) - df = pd.DataFrame(columns = cols, index = data['date'].unique()) + cols = pd.MultiIndex.from_product([self.columns.to_list(), data.columns[1:-1].to_list()]) + df = pd.DataFrame(columns=cols, index=data['date'].unique()) for col in cols: - symbol = data[data['symbol']==col[0]] + symbol = data[data['symbol'] == col[0]] symbol = symbol.set_index('date') - df[col[0],col[1]] = symbol[col[1]] + df[col[0], col[1]] = symbol[col[1]] self.data_symbol = df def sma(self, sma_days): - df = pd.DataFrame(columns = self.columns) + df = pd.DataFrame(columns=self.columns) for col in self.columns: - df[col] = self.data_symbol[col]['Adj Close'].rolling(sma_days, min_periods = 10).mean() + df[col] = self.data_symbol[col]['Adj Close'].rolling(sma_days, min_periods=10).mean() return df - - - diff --git a/asset_backtester/stats/charts.py b/asset_backtester/charts.py similarity index 87% rename from asset_backtester/stats/charts.py rename to asset_backtester/charts.py index 5186e34..368b2d4 100644 --- a/asset_backtester/stats/charts.py +++ b/asset_backtester/charts.py @@ -3,6 +3,7 @@ import altair as alt import pandas as pd + def returns_chart(report): # Time interval selector time_interval = alt.selection(type='interval', encodings=['x']) @@ -61,20 +62,18 @@ def monthly_returns_heatmap(report): return chart + def historical_values(data_sma, data_symbol, asset_name): - + asset_sma = pd.DataFrame(data_sma[asset_name]) - asset_sma = asset_sma.rename(columns = {asset_name:'value'}) - asset_sma['id'] = ['sma value']*(len(asset_sma.index)) + asset_sma = asset_sma.rename(columns={asset_name: 'value'}) + asset_sma['id'] = ['sma value'] * (len(asset_sma.index)) asset_sma = asset_sma.dropna() asset_value = pd.DataFrame(data_symbol[asset_name]['Adj Close']) - asset_value= asset_value.rename(columns={'Adj Close' :'value'}) - asset_value['id'] = ['Adj Close']*(len(asset_value.index)) + asset_value = asset_value.rename(columns={'Adj Close': 'value'}) + asset_value['id'] = ['Adj Close'] * (len(asset_value.index)) asset_value = asset_value.append(asset_sma) asset_value['index'] = asset_value.index - plot = alt.Chart(asset_value).mark_line().encode(x='index:T', - y=alt.Y('value:Q'), - color='id' -) - return plot \ No newline at end of file + plot = alt.Chart(asset_value).mark_line().encode(x='index:T', y=alt.Y('value:Q'), color='id') + return plot diff --git a/asset_backtester/datahandler/schema.py b/asset_backtester/datahandler/schema.py index 049599f..12b8402 100644 --- a/asset_backtester/datahandler/schema.py +++ b/asset_backtester/datahandler/schema.py @@ -3,7 +3,10 @@ class Schema: Used to run validations and provide uniform access to fields in the data set. """ - columns = ["symbol", "date", "open", "close", "high", "low", "volume", "Adj Close"] + columns = [ + "symbol", "date", "open", "close", "high", "low", "volume", "adjClose", "adjHigh", "adjLow", "adjOpen", + "adjVolume", "divCash", "splitFactor" + ] def canonical(): """Builder method that returns a `Schema` with default mappings""" diff --git a/asset_backtester/examples/example.ipynb b/asset_backtester/examples/example.ipynb index f55e5b4..0a0db42 100644 --- a/asset_backtester/examples/example.ipynb +++ b/asset_backtester/examples/example.ipynb @@ -6,9 +6,11 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os" + "import os\n", + "import sys\n", + "\n", + "backtester_dir = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))\n", + "sys.path.append(backtester_dir) # Add backtester base dir to $PYTHONPATH" ] }, { @@ -17,11 +19,9 @@ "metadata": {}, "outputs": [], "source": [ - "from backtester import Backtest\n", - "from portfolio.portfolio import Portfolio\n", - "from portfolio.asset import Asset\n", - "from datahandler import HistoricalAssetData\n", - "from statistics.charts import returns_chart, returns_histogram, monthly_returns_heatmap" + "from asset_backtester import Backtest, Portfolio, Asset\n", + "from asset_backtester.datahandler import HistoricalAssetData\n", + "from asset_backtester.charts import *" ] }, { @@ -30,17 +30,7 @@ "metadata": {}, "outputs": [], "source": [ - "%config InlineBackend.figure_format=\"retina\"\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas_datareader.data as web\n", + "import pandas_datareader as pdr\n", "import datetime" ] }, @@ -48,74 +38,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As a first example, we run a backtest of a portfolio with the following tickers during the year 2019. Data is taken from yahoo finance." + "As a first example, we run a backtest of a portfolio with the following tickers during the year 2019. Data is taken from [Tiingo](https://api.tiingo.com)." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: TIINGO_API_KEY=your_tiingo_api_key\n" + ] + } + ], + "source": [ + "%env TIINGO_API_KEY=your_tiingo_api_key" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ + "api_key = os.environ[\"TIINGO_API_KEY\"]\n", + "\n", "start = datetime.datetime(2019, 1, 1)\n", "end = datetime.datetime(2019, 12, 31)\n", - "\n", "tickers = [\"VOO\", \"TUR\", \"RSX\", \"EWY\", \"EWS\", \"VTIP\", \"TLT\", \"BWX\", \"PDBC\", \"IAU\", \"VNQI\"]\n", "\n", - "dfs = []\n", - "for ticker in tickers:\n", - " df = web.DataReader(ticker, 'yahoo', start, end).reset_index().rename(columns={'Date': 'date', 'High': 'high', 'Low': 'low', 'Open': 'open', 'Close': 'close', 'Volume': 'volume'})\n", - " df['symbol'] = ticker\n", - " dfs.append(df)\n" + "symbols = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.concat(dfs)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.sort_values(by='date').reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "df.to_csv(\"data/portfolio_data.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use *HistoricalAssetData* to load your csv. Data must include 'date', 'Adj Close' and 'symbol' columns to work." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "data = HistoricalAssetData(\"data/portfolio_data.csv\")\n", - "schema = data.schema" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -139,74 +99,121 @@ " \n", " \n", " \n", - " date\n", + " \n", + " close\n", " high\n", " low\n", " open\n", - " close\n", " volume\n", - " Adj Close\n", + " adjClose\n", + " adjHigh\n", + " adjLow\n", + " adjOpen\n", + " adjVolume\n", + " divCash\n", + " splitFactor\n", + " \n", + " \n", " symbol\n", + " date\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 0\n", - " 2019-01-02\n", - " 230.850006\n", - " 226.020004\n", - " 226.179993\n", - " 229.990005\n", - " 4891300.0\n", - " 225.350449\n", - " VOO\n", + " VOO\n", + " 2019-01-02 00:00:00+00:00\n", + " 229.99\n", + " 230.85\n", + " 226.02\n", + " 226.18\n", + " 4891329\n", + " 225.367394\n", + " 226.210108\n", + " 221.477187\n", + " 221.633971\n", + " 4891329\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 1\n", - " 2019-01-02\n", - " 15.333000\n", - " 14.890000\n", - " 14.920000\n", - " 15.120000\n", - " 1382500.0\n", - " 14.909319\n", - " PDBC\n", + " 2019-01-03 00:00:00+00:00\n", + " 224.50\n", + " 228.42\n", + " 223.97\n", + " 228.10\n", + " 3330026\n", + " 219.987738\n", + " 223.828949\n", + " 219.468391\n", + " 223.515381\n", + " 3330026\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2\n", - " 2019-01-02\n", - " 122.160004\n", - " 121.339996\n", - " 121.660004\n", - " 122.150002\n", - " 19841500.0\n", - " 119.344078\n", - " TLT\n", + " 2019-01-04 00:00:00+00:00\n", + " 231.91\n", + " 232.62\n", + " 227.15\n", + " 227.54\n", + " 5100088\n", + " 227.248803\n", + " 227.944533\n", + " 222.584475\n", + " 222.966637\n", + " 5100088\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 3\n", - " 2019-01-02\n", - " 47.910000\n", - " 47.860001\n", - " 47.860001\n", - " 47.910000\n", - " 771400.0\n", - " 46.986149\n", - " VTIP\n", + " 2019-01-07 00:00:00+00:00\n", + " 233.65\n", + " 235.23\n", + " 231.32\n", + " 232.29\n", + " 3706014\n", + " 228.953831\n", + " 230.502074\n", + " 226.670662\n", + " 227.621166\n", + " 3706014\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 4\n", - " 2019-01-02\n", - " 22.100000\n", - " 21.850000\n", - " 21.930000\n", - " 22.100000\n", - " 876200.0\n", - " 21.076557\n", - " EWS\n", + " 2019-01-08 00:00:00+00:00\n", + " 235.92\n", + " 236.46\n", + " 233.43\n", + " 236.05\n", + " 3546649\n", + " 231.178206\n", + " 231.707352\n", + " 228.738253\n", + " 231.305593\n", + " 3546649\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -217,96 +224,448 @@ " ...\n", " \n", " \n", - " 2767\n", - " 2019-12-31\n", - " 27.180000\n", - " 27.000000\n", - " 27.110001\n", - " 27.090000\n", - " 132900.0\n", - " 27.090000\n", - " TUR\n", + " VNQI\n", + " 2019-12-24 00:00:00+00:00\n", + " 58.22\n", + " 58.23\n", + " 58.08\n", + " 58.12\n", + " 637517\n", + " 58.220000\n", + " 58.230000\n", + " 58.080000\n", + " 58.120000\n", + " 637517\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2768\n", - " 2019-12-31\n", - " 16.650000\n", - " 16.510000\n", - " 16.520000\n", - " 16.559999\n", - " 1780800.0\n", - " 16.559999\n", - " PDBC\n", + " 2019-12-26 00:00:00+00:00\n", + " 58.54\n", + " 58.54\n", + " 58.25\n", + " 58.28\n", + " 355091\n", + " 58.540000\n", + " 58.540000\n", + " 58.250000\n", + " 58.280000\n", + " 355091\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2769\n", - " 2019-12-31\n", - " 295.989990\n", - " 294.170013\n", - " 294.529999\n", - " 295.799988\n", - " 2512700.0\n", - " 295.799988\n", - " VOO\n", + " 2019-12-27 00:00:00+00:00\n", + " 58.96\n", + " 58.96\n", + " 58.77\n", + " 58.78\n", + " 322343\n", + " 58.960000\n", + " 58.960000\n", + " 58.770000\n", + " 58.780000\n", + " 322343\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2770\n", - " 2019-12-31\n", - " 136.460007\n", - " 135.380005\n", - " 136.210007\n", - " 135.479996\n", - " 10707400.0\n", - " 135.479996\n", - " TLT\n", + " 2019-12-30 00:00:00+00:00\n", + " 58.71\n", + " 59.02\n", + " 58.71\n", + " 59.00\n", + " 294134\n", + " 58.710000\n", + " 59.020000\n", + " 58.710000\n", + " 59.000000\n", + " 294134\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2771\n", - " 2019-12-31\n", + " 2019-12-31 00:00:00+00:00\n", + " 59.09\n", + " 59.09\n", + " 58.85\n", + " 58.95\n", + " 219935\n", " 59.090000\n", - " 58.849998\n", - " 58.950001\n", " 59.090000\n", - " 219900.0\n", - " 59.090000\n", - " VNQI\n", + " 58.850000\n", + " 58.950000\n", + " 219935\n", + " 0.0\n", + " 1.0\n", " \n", " \n", "\n", - "

2772 rows × 8 columns

\n", + "

2772 rows × 12 columns

\n", "" ], "text/plain": [ - " date high low open close volume \\\n", - "0 2019-01-02 230.850006 226.020004 226.179993 229.990005 4891300.0 \n", - "1 2019-01-02 15.333000 14.890000 14.920000 15.120000 1382500.0 \n", - "2 2019-01-02 122.160004 121.339996 121.660004 122.150002 19841500.0 \n", - "3 2019-01-02 47.910000 47.860001 47.860001 47.910000 771400.0 \n", - "4 2019-01-02 22.100000 21.850000 21.930000 22.100000 876200.0 \n", - "... ... ... ... ... ... ... \n", - "2767 2019-12-31 27.180000 27.000000 27.110001 27.090000 132900.0 \n", - "2768 2019-12-31 16.650000 16.510000 16.520000 16.559999 1780800.0 \n", - "2769 2019-12-31 295.989990 294.170013 294.529999 295.799988 2512700.0 \n", - "2770 2019-12-31 136.460007 135.380005 136.210007 135.479996 10707400.0 \n", - "2771 2019-12-31 59.090000 58.849998 58.950001 59.090000 219900.0 \n", + " close high low open volume \\\n", + "symbol date \n", + "VOO 2019-01-02 00:00:00+00:00 229.99 230.85 226.02 226.18 4891329 \n", + " 2019-01-03 00:00:00+00:00 224.50 228.42 223.97 228.10 3330026 \n", + " 2019-01-04 00:00:00+00:00 231.91 232.62 227.15 227.54 5100088 \n", + " 2019-01-07 00:00:00+00:00 233.65 235.23 231.32 232.29 3706014 \n", + " 2019-01-08 00:00:00+00:00 235.92 236.46 233.43 236.05 3546649 \n", + "... ... ... ... ... ... \n", + "VNQI 2019-12-24 00:00:00+00:00 58.22 58.23 58.08 58.12 637517 \n", + " 2019-12-26 00:00:00+00:00 58.54 58.54 58.25 58.28 355091 \n", + " 2019-12-27 00:00:00+00:00 58.96 58.96 58.77 58.78 322343 \n", + " 2019-12-30 00:00:00+00:00 58.71 59.02 58.71 59.00 294134 \n", + " 2019-12-31 00:00:00+00:00 59.09 59.09 58.85 58.95 219935 \n", "\n", - " Adj Close symbol \n", - "0 225.350449 VOO \n", - "1 14.909319 PDBC \n", - "2 119.344078 TLT \n", - "3 46.986149 VTIP \n", - "4 21.076557 EWS \n", - "... ... ... \n", - "2767 27.090000 TUR \n", - "2768 16.559999 PDBC \n", - "2769 295.799988 VOO \n", - "2770 135.479996 TLT \n", - "2771 59.090000 VNQI \n", + " adjClose adjHigh adjLow \\\n", + "symbol date \n", + "VOO 2019-01-02 00:00:00+00:00 225.367394 226.210108 221.477187 \n", + " 2019-01-03 00:00:00+00:00 219.987738 223.828949 219.468391 \n", + " 2019-01-04 00:00:00+00:00 227.248803 227.944533 222.584475 \n", + " 2019-01-07 00:00:00+00:00 228.953831 230.502074 226.670662 \n", + " 2019-01-08 00:00:00+00:00 231.178206 231.707352 228.738253 \n", + "... ... ... ... \n", + "VNQI 2019-12-24 00:00:00+00:00 58.220000 58.230000 58.080000 \n", + " 2019-12-26 00:00:00+00:00 58.540000 58.540000 58.250000 \n", + " 2019-12-27 00:00:00+00:00 58.960000 58.960000 58.770000 \n", + " 2019-12-30 00:00:00+00:00 58.710000 59.020000 58.710000 \n", + " 2019-12-31 00:00:00+00:00 59.090000 59.090000 58.850000 \n", "\n", - "[2772 rows x 8 columns]" + " adjOpen adjVolume divCash splitFactor \n", + "symbol date \n", + "VOO 2019-01-02 00:00:00+00:00 221.633971 4891329 0.0 1.0 \n", + " 2019-01-03 00:00:00+00:00 223.515381 3330026 0.0 1.0 \n", + " 2019-01-04 00:00:00+00:00 222.966637 5100088 0.0 1.0 \n", + " 2019-01-07 00:00:00+00:00 227.621166 3706014 0.0 1.0 \n", + " 2019-01-08 00:00:00+00:00 231.305593 3546649 0.0 1.0 \n", + "... ... ... ... ... \n", + "VNQI 2019-12-24 00:00:00+00:00 58.120000 637517 0.0 1.0 \n", + " 2019-12-26 00:00:00+00:00 58.280000 355091 0.0 1.0 \n", + " 2019-12-27 00:00:00+00:00 58.780000 322343 0.0 1.0 \n", + " 2019-12-30 00:00:00+00:00 59.000000 294134 0.0 1.0 \n", + " 2019-12-31 00:00:00+00:00 58.950000 219935 0.0 1.0 \n", + "\n", + "[2772 rows x 12 columns]" ] }, - "execution_count": 13, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "symbols" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.join(backtester_dir, 'data')\n", + "save_path = os.path.join(data_dir, 'portfolio_data.csv')\n", + "symbols.to_csv(save_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use *HistoricalAssetData* to load your csv. Data must include `date`, `adjClose` and `symbol` columns to work." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = HistoricalAssetData(save_path)\n", + "schema = data.schema" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symboldateclosehighlowopenvolumeadjCloseadjHighadjLowadjOpenadjVolumedivCashsplitFactor
0VOO2019-01-02 00:00:00+00:00229.99230.85226.02226.184891329225.367394226.210108221.477187221.63397148913290.01.0
1VOO2019-01-03 00:00:00+00:00224.50228.42223.97228.103330026219.987738223.828949219.468391223.51538133300260.01.0
2VOO2019-01-04 00:00:00+00:00231.91232.62227.15227.545100088227.248803227.944533222.584475222.96663751000880.01.0
3VOO2019-01-07 00:00:00+00:00233.65235.23231.32232.293706014228.953831230.502074226.670662227.62116637060140.01.0
4VOO2019-01-08 00:00:00+00:00235.92236.46233.43236.053546649231.178206231.707352228.738253231.30559335466490.01.0
.............................................
2767VNQI2019-12-24 00:00:00+00:0058.2258.2358.0858.1263751758.22000058.23000058.08000058.1200006375170.01.0
2768VNQI2019-12-26 00:00:00+00:0058.5458.5458.2558.2835509158.54000058.54000058.25000058.2800003550910.01.0
2769VNQI2019-12-27 00:00:00+00:0058.9658.9658.7758.7832234358.96000058.96000058.77000058.7800003223430.01.0
2770VNQI2019-12-30 00:00:00+00:0058.7159.0258.7159.0029413458.71000059.02000058.71000059.0000002941340.01.0
2771VNQI2019-12-31 00:00:00+00:0059.0959.0958.8558.9521993559.09000059.09000058.85000058.9500002199350.01.0
\n", + "

2772 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " symbol date close high low open \\\n", + "0 VOO 2019-01-02 00:00:00+00:00 229.99 230.85 226.02 226.18 \n", + "1 VOO 2019-01-03 00:00:00+00:00 224.50 228.42 223.97 228.10 \n", + "2 VOO 2019-01-04 00:00:00+00:00 231.91 232.62 227.15 227.54 \n", + "3 VOO 2019-01-07 00:00:00+00:00 233.65 235.23 231.32 232.29 \n", + "4 VOO 2019-01-08 00:00:00+00:00 235.92 236.46 233.43 236.05 \n", + "... ... ... ... ... ... ... \n", + "2767 VNQI 2019-12-24 00:00:00+00:00 58.22 58.23 58.08 58.12 \n", + "2768 VNQI 2019-12-26 00:00:00+00:00 58.54 58.54 58.25 58.28 \n", + "2769 VNQI 2019-12-27 00:00:00+00:00 58.96 58.96 58.77 58.78 \n", + "2770 VNQI 2019-12-30 00:00:00+00:00 58.71 59.02 58.71 59.00 \n", + "2771 VNQI 2019-12-31 00:00:00+00:00 59.09 59.09 58.85 58.95 \n", + "\n", + " volume adjClose adjHigh adjLow adjOpen adjVolume \\\n", + "0 4891329 225.367394 226.210108 221.477187 221.633971 4891329 \n", + "1 3330026 219.987738 223.828949 219.468391 223.515381 3330026 \n", + "2 5100088 227.248803 227.944533 222.584475 222.966637 5100088 \n", + "3 3706014 228.953831 230.502074 226.670662 227.621166 3706014 \n", + "4 3546649 231.178206 231.707352 228.738253 231.305593 3546649 \n", + "... ... ... ... ... ... ... \n", + "2767 637517 58.220000 58.230000 58.080000 58.120000 637517 \n", + "2768 355091 58.540000 58.540000 58.250000 58.280000 355091 \n", + "2769 322343 58.960000 58.960000 58.770000 58.780000 322343 \n", + "2770 294134 58.710000 59.020000 58.710000 59.000000 294134 \n", + "2771 219935 59.090000 59.090000 58.850000 58.950000 219935 \n", + "\n", + " divCash splitFactor \n", + "0 0.0 1.0 \n", + "1 0.0 1.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + "... ... ... \n", + "2767 0.0 1.0 \n", + "2768 0.0 1.0 \n", + "2769 0.0 1.0 \n", + "2770 0.0 1.0 \n", + "2771 0.0 1.0 \n", + "\n", + "[2772 rows x 14 columns]" + ] + }, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -319,12 +678,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To create a portfolio, use the Portfolio class and then create assets with Asset(*name*, *percentage*), where *name* should match the name given in the 'symbol' column and *percentage* is the percentage (from 0 to 1) allocated to that specific asset." + "To create a portfolio, use the Portfolio class and then create assets with Asset(*name*, *percentage*), where *name* should match the name given in the `symbol` column and *percentage* is the percentage (from 0 to 1) allocated to that specific asset." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -352,16 +711,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -382,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -401,7 +760,7 @@ "output_type": "stream", "text": [ "0% [██████████████████████████████] 100% | ETA: 00:00:00\n", - "Total time elapsed: 00:00:04\n" + "Total time elapsed: 00:00:10\n" ] }, { @@ -434,7 +793,7 @@ " \n", " \n", " \n", - " 2019-01-01\n", + " 2019-01-01 00:00:00+00:00\n", " 1.000000e+06\n", " 1000000.000000\n", " NaN\n", @@ -442,39 +801,39 @@ " NaN\n", " \n", " \n", - " 2019-01-02\n", + " 2019-01-02 00:00:00+00:00\n", " 1.000000e+06\n", - " 403.112655\n", - " 9.995969e+05\n", + " 365.102886\n", + " 9.996349e+05\n", " 0.000000\n", " 1.000000\n", " \n", " \n", - " 2019-01-03\n", - " 9.987008e+05\n", - " 403.112655\n", - " 9.982976e+05\n", - " -0.001299\n", - " 0.998701\n", + " 2019-01-03 00:00:00+00:00\n", + " 9.987002e+05\n", + " 365.102886\n", + " 9.983351e+05\n", + " -0.001300\n", + " 0.998700\n", " \n", " \n", - " 2019-01-04\n", + " 2019-01-04 00:00:00+00:00\n", " 1.008706e+06\n", - " 403.112655\n", - " 1.008302e+06\n", - " 0.010018\n", + " 365.102886\n", + " 1.008341e+06\n", + " 0.010019\n", " 1.008706\n", " \n", " \n", - " 2019-01-07\n", - " 1.010929e+06\n", - " 403.112655\n", - " 1.010526e+06\n", + " 2019-01-07 00:00:00+00:00\n", + " 1.010930e+06\n", + " 365.102886\n", + " 1.010565e+06\n", " 0.002205\n", - " 1.010929\n", + " 1.010930\n", " \n", " \n", - " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -482,44 +841,44 @@ " ...\n", " \n", " \n", - " 2019-12-24\n", - " 1.158018e+06\n", - " 403.112655\n", - " 1.157615e+06\n", + " 2019-12-24 00:00:00+00:00\n", + " 1.157945e+06\n", + " 365.102886\n", + " 1.157580e+06\n", " 0.002707\n", - " 1.158018\n", + " 1.157945\n", " \n", " \n", - " 2019-12-26\n", - " 1.163238e+06\n", - " 403.112655\n", - " 1.162835e+06\n", + " 2019-12-26 00:00:00+00:00\n", + " 1.163165e+06\n", + " 365.102886\n", + " 1.162800e+06\n", " 0.004508\n", - " 1.163238\n", + " 1.163165\n", " \n", " \n", - " 2019-12-27\n", - " 1.165936e+06\n", - " 403.112655\n", - " 1.165533e+06\n", + " 2019-12-27 00:00:00+00:00\n", + " 1.165863e+06\n", + " 365.102886\n", + " 1.165498e+06\n", " 0.002319\n", - " 1.165936\n", + " 1.165863\n", " \n", " \n", - " 2019-12-30\n", - " 1.163417e+06\n", - " 403.112655\n", - " 1.163014e+06\n", - " -0.002160\n", - " 1.163417\n", + " 2019-12-30 00:00:00+00:00\n", + " 1.163344e+06\n", + " 365.102886\n", + " 1.162979e+06\n", + " -0.002161\n", + " 1.163344\n", " \n", " \n", - " 2019-12-31\n", - " 1.163667e+06\n", - " 403.112655\n", - " 1.163264e+06\n", + " 2019-12-31 00:00:00+00:00\n", + " 1.163594e+06\n", + " 365.102886\n", + " 1.163229e+06\n", " 0.000215\n", - " 1.163667\n", + " 1.163594\n", " \n", " \n", "\n", @@ -527,36 +886,36 @@ "" ], "text/plain": [ - " capital cash total_value % change \\\n", - "2019-01-01 1.000000e+06 1000000.000000 NaN NaN \n", - "2019-01-02 1.000000e+06 403.112655 9.995969e+05 0.000000 \n", - "2019-01-03 9.987008e+05 403.112655 9.982976e+05 -0.001299 \n", - "2019-01-04 1.008706e+06 403.112655 1.008302e+06 0.010018 \n", - "2019-01-07 1.010929e+06 403.112655 1.010526e+06 0.002205 \n", - "... ... ... ... ... \n", - "2019-12-24 1.158018e+06 403.112655 1.157615e+06 0.002707 \n", - "2019-12-26 1.163238e+06 403.112655 1.162835e+06 0.004508 \n", - "2019-12-27 1.165936e+06 403.112655 1.165533e+06 0.002319 \n", - "2019-12-30 1.163417e+06 403.112655 1.163014e+06 -0.002160 \n", - "2019-12-31 1.163667e+06 403.112655 1.163264e+06 0.000215 \n", + " capital cash total_value \\\n", + "2019-01-01 00:00:00+00:00 1.000000e+06 1000000.000000 NaN \n", + "2019-01-02 00:00:00+00:00 1.000000e+06 365.102886 9.996349e+05 \n", + "2019-01-03 00:00:00+00:00 9.987002e+05 365.102886 9.983351e+05 \n", + "2019-01-04 00:00:00+00:00 1.008706e+06 365.102886 1.008341e+06 \n", + "2019-01-07 00:00:00+00:00 1.010930e+06 365.102886 1.010565e+06 \n", + "... ... ... ... \n", + "2019-12-24 00:00:00+00:00 1.157945e+06 365.102886 1.157580e+06 \n", + "2019-12-26 00:00:00+00:00 1.163165e+06 365.102886 1.162800e+06 \n", + "2019-12-27 00:00:00+00:00 1.165863e+06 365.102886 1.165498e+06 \n", + "2019-12-30 00:00:00+00:00 1.163344e+06 365.102886 1.162979e+06 \n", + "2019-12-31 00:00:00+00:00 1.163594e+06 365.102886 1.163229e+06 \n", "\n", - " accumulated return \n", - "2019-01-01 NaN \n", - "2019-01-02 1.000000 \n", - "2019-01-03 0.998701 \n", - "2019-01-04 1.008706 \n", - "2019-01-07 1.010929 \n", - "... ... \n", - "2019-12-24 1.158018 \n", - "2019-12-26 1.163238 \n", - "2019-12-27 1.165936 \n", - "2019-12-30 1.163417 \n", - "2019-12-31 1.163667 \n", + " % change accumulated return \n", + "2019-01-01 00:00:00+00:00 NaN NaN \n", + "2019-01-02 00:00:00+00:00 0.000000 1.000000 \n", + "2019-01-03 00:00:00+00:00 -0.001300 0.998700 \n", + "2019-01-04 00:00:00+00:00 0.010019 1.008706 \n", + "2019-01-07 00:00:00+00:00 0.002205 1.010930 \n", + "... ... ... \n", + "2019-12-24 00:00:00+00:00 0.002707 1.157945 \n", + "2019-12-26 00:00:00+00:00 0.004508 1.163165 \n", + "2019-12-27 00:00:00+00:00 0.002319 1.165863 \n", + "2019-12-30 00:00:00+00:00 -0.002161 1.163344 \n", + "2019-12-31 00:00:00+00:00 0.000215 1.163594 \n", "\n", "[253 rows x 5 columns]" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -575,2205 +934,65 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-ef5050b5e3116916582ad77761dab043" - }, - "datasets": { - "data-ef5050b5e3116916582ad77761dab043": [ - 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For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
\n", + "" + ], + "text/plain": [ + "alt.VConcatChart(...)" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2784,1828 +1003,65 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-982132847eb7f89b37515d2a7bf99e0b" - }, - "datasets": { - "data-982132847eb7f89b37515d2a7bf99e0b": [ - { - "% change": null, - "accumulated return": null, - "capital": 1000000, - "cash": 1000000, - "total_value": null - }, - { - "% change": 0, - "accumulated return": 1, - "capital": 1000000, - "cash": 403.1126546859741, - "total_value": 999596.887345314 - }, - { - "% change": -0.0012992443809509702, - "accumulated return": 0.998700755619049, - 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For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" ] }, - "execution_count": 20, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4616,119 +1072,65 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-a50706b6a0cbadc750008fa29fa8466b" - }, - "datasets": { - "data-a50706b6a0cbadc750008fa29fa8466b": [ - { - "capital": 0.0527641227903366, - "date": "2019-01-31T00:00:00" - }, - { - "capital": -0.006040739508118564, - "date": "2019-02-28T00:00:00" - }, - { - "capital": 0.008771790157011461, - "date": "2019-03-31T00:00:00" - }, - { - "capital": 0.0018936451648277064, - "date": "2019-04-30T00:00:00" - }, - { - "capital": -0.005253987600556176, - "date": "2019-05-31T00:00:00" - }, - { - "capital": 0.046842661523986306, - "date": "2019-06-30T00:00:00" - }, - { - "capital": -0.0019322180432722602, - "date": "2019-07-31T00:00:00" - }, - { - "capital": 0.020659376002570395, - "date": "2019-08-31T00:00:00" - }, - { - "capital": 0.0018937636047886564, - "date": "2019-09-30T00:00:00" - }, - { - "capital": 0.013238011026400764, - "date": "2019-10-31T00:00:00" - }, - { - "capital": -0.0012322601976857417, - "date": "2019-11-30T00:00:00" - }, - { - "capital": 0.02301614440273414, - "date": "2019-12-31T00:00:00" - } - ] - }, - "encoding": { - "color": { - "aggregate": "mean", - "field": "capital", - "scale": { - "scheme": "redyellowgreen" - }, - "title": "Return", - "type": "quantitative" - }, - "tooltip": { - "aggregate": "mean", - "field": "capital", - "format": ".2f", - "type": "quantitative" - }, - "x": { - "field": "date", - "timeUnit": "year", - "title": "Year", - "type": "ordinal" - }, - "y": { - "field": "date", - "timeUnit": "month", - "title": "Month", - "type": "ordinal" - } - }, - "mark": "rect", - "title": "Monthly Returns" - }, - "image/png": 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", - "text/plain": [ - "\n", + "text/html": [ "\n", - "If you see this message, it means the renderer has not been properly enabled\n", - "for the frontend that you are using. For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" ] }, - "execution_count": 21, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4743,27 +1145,20 @@ "source": [ "We now run a backtest of a portfolio from the book \"The Ivy Portfolio\" by Faber and Richardson, consisting of an even allocation in domestic (US) stocks (VTI), foreign stocks (VEU), bonds (BND), real estate (VNQ) and commodities (DBC).\n", "\n", - "The data we'll use consists of the last 10 years (2010-2019), once again taken from yahoo finance." + "The data we'll use consists of the last 10 years (2010-2019), once again taken from Tiingo." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "tickers = [\"VTI\", \"VEU\", \"BND\", \"VNQ\", \"DBC\"]\n", + "start = datetime.datetime(2010, 1, 1)\n", + "end = datetime.datetime(2019, 12, 31)\n", "\n", - "dfs = []\n", - "for ticker in tickers:\n", - " start = datetime.datetime(2020, 1, 1)\n", - " end = datetime.datetime(2020, 12, 31)\n", - " for i in range(10):\n", - " start = start.replace(year = start.year - 1)\n", - " end = end.replace(year = end.year - 1)\n", - " df = web.DataReader(ticker, 'yahoo', start, end).reset_index().rename(columns={'Date': 'date', 'High': 'high', 'Low': 'low', 'Open': 'open', 'Close': 'close', 'Volume': 'volume'})\n", - " df['symbol'] = ticker\n", - " dfs.append(df)\n" + "ivy_data = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)" ] }, { @@ -4772,7 +1167,8 @@ "metadata": {}, "outputs": [], "source": [ - "ivy_df = pd.concat(dfs)" + "ivy_data_path = os.path.join(data_dir, 'ivy_portfolio_data.csv')\n", + "ivy_data.to_csv(ivy_data_path)" ] }, { @@ -4781,38 +1177,13 @@ "metadata": {}, "outputs": [], "source": [ - "ivy_df = ivy_df.sort_values(by='date').reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "ivy_df.to_csv(\"data/ivy_portfolio_data.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "data = HistoricalAssetData(\"data/ivy_portfolio_data.csv\")\n", + "data = HistoricalAssetData(ivy_data_path)\n", "schema = data.schema" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -4836,174 +1207,259 @@ " \n", " \n", " \n", + " symbol\n", " date\n", + " close\n", " high\n", " low\n", " open\n", - " close\n", " volume\n", - " Adj Close\n", - " symbol\n", + " adjClose\n", + " adjHigh\n", + " adjLow\n", + " adjOpen\n", + " adjVolume\n", + " divCash\n", + " splitFactor\n", " \n", " \n", " \n", " \n", - " 0\n", - " 2010-01-04\n", - " 57.380001\n", - " 56.840000\n", - " 56.860001\n", - " 57.310001\n", - " 2251500.0\n", - " 47.202740\n", + " 0\n", " VTI\n", + " 2010-01-04 00:00:00+00:00\n", + " 57.31\n", + " 57.3799\n", + " 56.84\n", + " 56.86\n", + " 2251461\n", + " 47.189147\n", + " 47.246703\n", + " 46.802149\n", + " 46.818617\n", + " 2251461\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 1\n", - " 2010-01-04\n", - " 78.730003\n", - " 78.540001\n", - " 78.599998\n", - " 78.680000\n", - " 1098100.0\n", - " 58.797794\n", - " BND\n", + " 1\n", + " VTI\n", + " 2010-01-05 00:00:00+00:00\n", + " 57.53\n", + " 57.5400\n", + " 57.11\n", + " 57.34\n", + " 1597643\n", + " 47.370296\n", + " 47.378530\n", + " 47.024467\n", + " 47.213849\n", + " 1597643\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 2\n", - " 2010-01-04\n", - " 44.959999\n", - " 44.400002\n", - " 44.660000\n", - " 44.959999\n", - " 1692500.0\n", - " 33.440495\n", - " VEU\n", + " 2\n", + " VTI\n", + " 2010-01-06 00:00:00+00:00\n", + " 57.61\n", + " 57.7150\n", + " 57.41\n", + " 57.50\n", + " 2120206\n", + " 47.436168\n", + " 47.522625\n", + " 47.271488\n", + " 47.345594\n", + " 2120206\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 3\n", - " 2010-01-04\n", - " 45.419998\n", - " 44.200001\n", - " 45.220001\n", - " 44.549999\n", - " 2408400.0\n", - " 29.869335\n", - " VNQ\n", + " 3\n", + " VTI\n", + " 2010-01-07 00:00:00+00:00\n", + " 57.85\n", + " 57.8890\n", + " 57.29\n", + " 57.55\n", + " 1656639\n", + " 47.633784\n", + " 47.665897\n", + " 47.172679\n", + " 47.386764\n", + " 1656639\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 4\n", - " 2010-01-04\n", - " 25.240000\n", - " 25.070000\n", - " 25.170000\n", - " 25.240000\n", - " 2046900.0\n", - " 24.523520\n", + " 4\n", + " VTI\n", + " 2010-01-08 00:00:00+00:00\n", + " 58.04\n", + " 58.0461\n", + " 57.56\n", + " 57.70\n", + " 1649919\n", + " 47.790231\n", + " 47.795253\n", + " 47.394998\n", + " 47.510274\n", + " 1649919\n", + " 0.0\n", + " 1.0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 12575\n", " DBC\n", + " 2019-12-24 00:00:00+00:00\n", + " 15.93\n", + " 15.9500\n", + " 15.86\n", + " 15.86\n", + " 466153\n", + " 15.930000\n", + " 15.950000\n", + " 15.860000\n", + " 15.860000\n", + " 466153\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 12576\n", + " DBC\n", + " 2019-12-26 00:00:00+00:00\n", + " 16.05\n", + " 16.0500\n", + " 15.97\n", + " 15.97\n", + " 679206\n", + " 16.050000\n", + " 16.050000\n", + " 15.970000\n", + " 15.970000\n", + " 679206\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 12575\n", - " 2019-12-31\n", - " 16.049999\n", + " 12577\n", + " DBC\n", + " 2019-12-27 00:00:00+00:00\n", + " 16.08\n", + " 16.0900\n", + " 16.02\n", + " 16.06\n", + " 1250014\n", + " 16.080000\n", + " 16.090000\n", + " 16.020000\n", + " 16.060000\n", + " 1250014\n", + " 0.0\n", + " 1.0\n", + " \n", + " \n", + " 12578\n", + " DBC\n", + " 2019-12-30 00:00:00+00:00\n", + " 16.05\n", + " 16.1800\n", + " 15.98\n", + " 16.16\n", + " 976284\n", + " 16.050000\n", + " 16.180000\n", + " 15.980000\n", + " 16.160000\n", + " 976284\n", + " 0.0\n", + " 1.0\n", + " \n", + " \n", + " 12579\n", + " DBC\n", + " 2019-12-31 00:00:00+00:00\n", + " 15.95\n", + " 16.0453\n", + " 15.91\n", + " 15.95\n", + " 806115\n", + " 15.950000\n", + " 16.045300\n", " 15.910000\n", " 15.950000\n", - " 15.950000\n", - " 806100.0\n", - " 15.950000\n", - " DBC\n", - " \n", - " \n", - " 12576\n", - " 2019-12-31\n", - " 53.790001\n", - " 53.480000\n", - " 53.619999\n", - " 53.750000\n", - " 1688400.0\n", - " 53.750000\n", - " VEU\n", - " \n", - " \n", - " 12577\n", - " 2019-12-31\n", - " 92.830002\n", - " 92.000000\n", - " 92.089996\n", - " 92.790001\n", - " 4649500.0\n", - " 92.790001\n", - " VNQ\n", - " \n", - " \n", - " 12578\n", - " 2019-12-31\n", - " 83.919998\n", - " 83.769997\n", - " 83.849998\n", - " 83.860001\n", - " 5931300.0\n", - " 83.860001\n", - " BND\n", - " \n", - " \n", - " 12579\n", - " 2019-12-31\n", - " 163.759995\n", - " 162.720001\n", - " 163.000000\n", - " 163.619995\n", - " 3262400.0\n", - " 163.619995\n", - " VTI\n", + " 806115\n", + " 0.0\n", + " 1.0\n", " \n", " \n", "\n", - "

12580 rows × 8 columns

\n", + "

12580 rows × 14 columns

\n", "" ], "text/plain": [ - " date high low open close volume \\\n", - "0 2010-01-04 57.380001 56.840000 56.860001 57.310001 2251500.0 \n", - "1 2010-01-04 78.730003 78.540001 78.599998 78.680000 1098100.0 \n", - "2 2010-01-04 44.959999 44.400002 44.660000 44.959999 1692500.0 \n", - "3 2010-01-04 45.419998 44.200001 45.220001 44.549999 2408400.0 \n", - "4 2010-01-04 25.240000 25.070000 25.170000 25.240000 2046900.0 \n", - "... ... ... ... ... ... ... \n", - "12575 2019-12-31 16.049999 15.910000 15.950000 15.950000 806100.0 \n", - "12576 2019-12-31 53.790001 53.480000 53.619999 53.750000 1688400.0 \n", - "12577 2019-12-31 92.830002 92.000000 92.089996 92.790001 4649500.0 \n", - "12578 2019-12-31 83.919998 83.769997 83.849998 83.860001 5931300.0 \n", - "12579 2019-12-31 163.759995 162.720001 163.000000 163.619995 3262400.0 \n", + " symbol date close high low open volume \\\n", + "0 VTI 2010-01-04 00:00:00+00:00 57.31 57.3799 56.84 56.86 2251461 \n", + "1 VTI 2010-01-05 00:00:00+00:00 57.53 57.5400 57.11 57.34 1597643 \n", + "2 VTI 2010-01-06 00:00:00+00:00 57.61 57.7150 57.41 57.50 2120206 \n", + "3 VTI 2010-01-07 00:00:00+00:00 57.85 57.8890 57.29 57.55 1656639 \n", + "4 VTI 2010-01-08 00:00:00+00:00 58.04 58.0461 57.56 57.70 1649919 \n", + "... ... ... ... ... ... ... ... \n", + "12575 DBC 2019-12-24 00:00:00+00:00 15.93 15.9500 15.86 15.86 466153 \n", + "12576 DBC 2019-12-26 00:00:00+00:00 16.05 16.0500 15.97 15.97 679206 \n", + "12577 DBC 2019-12-27 00:00:00+00:00 16.08 16.0900 16.02 16.06 1250014 \n", + "12578 DBC 2019-12-30 00:00:00+00:00 16.05 16.1800 15.98 16.16 976284 \n", + "12579 DBC 2019-12-31 00:00:00+00:00 15.95 16.0453 15.91 15.95 806115 \n", "\n", - " Adj Close symbol \n", - "0 47.202740 VTI \n", - "1 58.797794 BND \n", - "2 33.440495 VEU \n", - "3 29.869335 VNQ \n", - "4 24.523520 DBC \n", - "... ... ... \n", - "12575 15.950000 DBC \n", - "12576 53.750000 VEU \n", - "12577 92.790001 VNQ \n", - "12578 83.860001 BND \n", - "12579 163.619995 VTI \n", + " adjClose adjHigh adjLow adjOpen adjVolume divCash \\\n", + "0 47.189147 47.246703 46.802149 46.818617 2251461 0.0 \n", + "1 47.370296 47.378530 47.024467 47.213849 1597643 0.0 \n", + "2 47.436168 47.522625 47.271488 47.345594 2120206 0.0 \n", + "3 47.633784 47.665897 47.172679 47.386764 1656639 0.0 \n", + "4 47.790231 47.795253 47.394998 47.510274 1649919 0.0 \n", + "... ... ... ... ... ... ... \n", + "12575 15.930000 15.950000 15.860000 15.860000 466153 0.0 \n", + "12576 16.050000 16.050000 15.970000 15.970000 679206 0.0 \n", + "12577 16.080000 16.090000 16.020000 16.060000 1250014 0.0 \n", + "12578 16.050000 16.180000 15.980000 16.160000 976284 0.0 \n", + "12579 15.950000 16.045300 15.910000 15.950000 806115 0.0 \n", "\n", - "[12580 rows x 8 columns]" + " splitFactor \n", + "0 1.0 \n", + "1 1.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + "... ... \n", + "12575 1.0 \n", + "12576 1.0 \n", + "12577 1.0 \n", + "12578 1.0 \n", + "12579 1.0 \n", + "\n", + "[12580 rows x 14 columns]" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -5014,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -5023,7 +1479,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -5036,16 +1492,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -5056,7 +1512,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -5067,7 +1523,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -5075,7 +1531,7 @@ "output_type": "stream", "text": [ "0% [██████████████████████████████] 100% | ETA: 00:00:00\n", - "Total time elapsed: 00:00:19\n" + "Total time elapsed: 00:00:46\n" ] }, { @@ -5108,7 +1564,7 @@ " \n", " \n", " \n", - " 2010-01-03\n", + " 2010-01-03 00:00:00+00:00\n", " 1.000000e+06\n", " 1000000.000000\n", " NaN\n", @@ -5116,39 +1572,39 @@ " NaN\n", " \n", " \n", - " 2010-01-04\n", + " 2010-01-04 00:00:00+00:00\n", " 1.000000e+06\n", - " 92.035288\n", - " 9.999080e+05\n", + " 96.545508\n", + " 9.999035e+05\n", " 0.000000\n", " 1.000000\n", " \n", " \n", - " 2010-01-05\n", + " 2010-01-05 00:00:00+00:00\n", " 1.001321e+06\n", - " 92.035288\n", - " 1.001229e+06\n", + " 96.545508\n", + " 1.001225e+06\n", " 0.001321\n", " 1.001321\n", " \n", " \n", - " 2010-01-06\n", - " 1.005620e+06\n", - " 92.035288\n", - " 1.005528e+06\n", - " 0.004293\n", - " 1.005620\n", + " 2010-01-06 00:00:00+00:00\n", + " 1.005603e+06\n", + " 96.545508\n", + " 1.005507e+06\n", + " 0.004276\n", + " 1.005603\n", " \n", " \n", - " 2010-01-07\n", + " 2010-01-07 00:00:00+00:00\n", " 1.004723e+06\n", - " 92.035288\n", - " 1.004631e+06\n", - " -0.000892\n", + " 96.545508\n", + " 1.004627e+06\n", + " -0.000875\n", " 1.004723\n", " \n", " \n", - " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -5156,44 +1612,44 @@ " ...\n", " \n", " \n", - " 2019-12-24\n", - " 2.039190e+06\n", - " 92.035288\n", - " 2.039098e+06\n", - " 0.001160\n", - " 2.039190\n", + " 2019-12-24 00:00:00+00:00\n", + " 2.039175e+06\n", + " 96.545508\n", + " 2.039079e+06\n", + " 0.001159\n", + " 2.039175\n", " \n", " \n", - " 2019-12-26\n", - " 2.048080e+06\n", - " 92.035288\n", - " 2.047988e+06\n", + " 2019-12-26 00:00:00+00:00\n", + " 2.048066e+06\n", + " 96.545508\n", + " 2.047969e+06\n", " 0.004360\n", - " 2.048080\n", + " 2.048066\n", " \n", " \n", - " 2019-12-27\n", - " 2.050816e+06\n", - " 92.035288\n", - " 2.050724e+06\n", + " 2019-12-27 00:00:00+00:00\n", + " 2.050801e+06\n", + " 96.545508\n", + " 2.050705e+06\n", " 0.001336\n", - " 2.050816\n", + " 2.050801\n", " \n", " \n", - " 2019-12-30\n", - " 2.045149e+06\n", - " 92.035288\n", - " 2.045056e+06\n", - " -0.002764\n", - " 2.045149\n", + " 2019-12-30 00:00:00+00:00\n", + " 2.045131e+06\n", + " 96.545508\n", + " 2.045035e+06\n", + " -0.002765\n", + " 2.045131\n", " \n", " \n", - " 2019-12-31\n", - " 2.051284e+06\n", - " 92.035288\n", - " 2.051192e+06\n", - " 0.003000\n", - " 2.051284\n", + " 2019-12-31 00:00:00+00:00\n", + " 2.051265e+06\n", + " 96.545508\n", + " 2.051168e+06\n", + " 0.002999\n", + " 2.051265\n", " \n", " \n", "\n", @@ -5201,20358 +1657,106 @@ "" ], "text/plain": [ - " capital cash total_value % change \\\n", - "2010-01-03 1.000000e+06 1000000.000000 NaN NaN \n", - "2010-01-04 1.000000e+06 92.035288 9.999080e+05 0.000000 \n", - "2010-01-05 1.001321e+06 92.035288 1.001229e+06 0.001321 \n", - "2010-01-06 1.005620e+06 92.035288 1.005528e+06 0.004293 \n", - "2010-01-07 1.004723e+06 92.035288 1.004631e+06 -0.000892 \n", - "... ... ... ... ... \n", - "2019-12-24 2.039190e+06 92.035288 2.039098e+06 0.001160 \n", - "2019-12-26 2.048080e+06 92.035288 2.047988e+06 0.004360 \n", - "2019-12-27 2.050816e+06 92.035288 2.050724e+06 0.001336 \n", - "2019-12-30 2.045149e+06 92.035288 2.045056e+06 -0.002764 \n", - "2019-12-31 2.051284e+06 92.035288 2.051192e+06 0.003000 \n", + " capital cash total_value \\\n", + "2010-01-03 00:00:00+00:00 1.000000e+06 1000000.000000 NaN \n", + "2010-01-04 00:00:00+00:00 1.000000e+06 96.545508 9.999035e+05 \n", + "2010-01-05 00:00:00+00:00 1.001321e+06 96.545508 1.001225e+06 \n", + "2010-01-06 00:00:00+00:00 1.005603e+06 96.545508 1.005507e+06 \n", + "2010-01-07 00:00:00+00:00 1.004723e+06 96.545508 1.004627e+06 \n", + "... ... ... ... \n", + "2019-12-24 00:00:00+00:00 2.039175e+06 96.545508 2.039079e+06 \n", + "2019-12-26 00:00:00+00:00 2.048066e+06 96.545508 2.047969e+06 \n", + "2019-12-27 00:00:00+00:00 2.050801e+06 96.545508 2.050705e+06 \n", + "2019-12-30 00:00:00+00:00 2.045131e+06 96.545508 2.045035e+06 \n", + "2019-12-31 00:00:00+00:00 2.051265e+06 96.545508 2.051168e+06 \n", "\n", - " accumulated return \n", - "2010-01-03 NaN \n", - "2010-01-04 1.000000 \n", - "2010-01-05 1.001321 \n", - "2010-01-06 1.005620 \n", - "2010-01-07 1.004723 \n", - "... ... \n", - "2019-12-24 2.039190 \n", - "2019-12-26 2.048080 \n", - "2019-12-27 2.050816 \n", - "2019-12-30 2.045149 \n", - "2019-12-31 2.051284 \n", + " % change accumulated return \n", + "2010-01-03 00:00:00+00:00 NaN NaN \n", + "2010-01-04 00:00:00+00:00 0.000000 1.000000 \n", + "2010-01-05 00:00:00+00:00 0.001321 1.001321 \n", + "2010-01-06 00:00:00+00:00 0.004276 1.005603 \n", + "2010-01-07 00:00:00+00:00 -0.000875 1.004723 \n", + "... ... ... \n", + "2019-12-24 00:00:00+00:00 0.001159 2.039175 \n", + "2019-12-26 00:00:00+00:00 0.004360 2.048066 \n", + "2019-12-27 00:00:00+00:00 0.001336 2.050801 \n", + "2019-12-30 00:00:00+00:00 -0.002765 2.045131 \n", + "2019-12-31 00:00:00+00:00 0.002999 2.051265 \n", "\n", "[2517 rows x 5 columns]" ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# No rebalancing\n", - "bt.run(initial_capital = 1_000_000, periods=None)" + "bt.run(initial_capital=1_000_000, periods=None)" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-aa5397100d0107d5841784718486f1f2" - }, - "datasets": { - "data-aa5397100d0107d5841784718486f1f2": [ - { - "% change": null, - "accumulated return": null, - "capital": 1000000, - "cash": 1000000, - "index": "2010-01-03T00:00:00", - "total_value": null - }, - { - "% change": 0, - "accumulated return": 1, - "capital": 1000000, - "cash": 92.03528785705566, - "index": "2010-01-04T00:00:00", - 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"title": "Return", - "type": "quantitative" - }, - "tooltip": { - "aggregate": "mean", - "field": "capital", - "format": ".2f", - "type": "quantitative" - }, - "x": { - "field": "date", - "timeUnit": "year", - "title": "Year", - "type": "ordinal" - }, - "y": { - "field": "date", - "timeUnit": "month", - "title": "Month", - "type": "ordinal" - } - }, - "mark": "rect", - "title": "Monthly Returns" - }, - "image/png": 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", - "text/plain": [ - "\n", + "text/html": [ "\n", - "If you see this message, it means the renderer has not been properly enabled\n", - "for the frontend that you are using. For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" ] }, - "execution_count": 34, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -26125,7 +1843,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -26133,7 +1851,7 @@ "output_type": "stream", "text": [ "0% [██████████████████████████████] 100% | ETA: 00:00:00\n", - "Total time elapsed: 00:00:22\n" + "Total time elapsed: 00:00:51\n" ] }, { @@ -26166,7 +1884,7 @@ " \n", " \n", " \n", - " 2010-01-03\n", + " 2010-01-03 00:00:00+00:00\n", " 1.000000e+06\n", " 1000000.000000\n", " NaN\n", @@ -26174,39 +1892,39 @@ " NaN\n", " \n", " \n", - " 2010-01-04\n", + " 2010-01-04 00:00:00+00:00\n", " 1.000000e+06\n", - " 92.035288\n", - " 9.999080e+05\n", + " 96.545508\n", + " 9.999035e+05\n", " 0.000000\n", " 1.000000\n", " \n", " \n", - " 2010-01-05\n", + " 2010-01-05 00:00:00+00:00\n", " 1.001321e+06\n", - " 92.035288\n", - " 1.001229e+06\n", + " 96.545508\n", + " 1.001225e+06\n", " 0.001321\n", " 1.001321\n", " \n", " \n", - " 2010-01-06\n", - " 1.005620e+06\n", - " 92.035288\n", - " 1.005528e+06\n", - " 0.004293\n", - " 1.005620\n", + " 2010-01-06 00:00:00+00:00\n", + " 1.005603e+06\n", + " 96.545508\n", + " 1.005507e+06\n", + " 0.004276\n", + " 1.005603\n", " \n", " \n", - " 2010-01-07\n", + " 2010-01-07 00:00:00+00:00\n", " 1.004723e+06\n", - " 92.035288\n", - " 1.004631e+06\n", - " -0.000892\n", + " 96.545508\n", + " 1.004627e+06\n", + " -0.000875\n", " 1.004723\n", " \n", " \n", - " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -26214,44 +1932,44 @@ " ...\n", " \n", " \n", - " 2019-12-24\n", - " 1.817790e+06\n", - " 57.640553\n", - " 1.817733e+06\n", - " 0.001500\n", - " 1.817790\n", + " 2019-12-24 00:00:00+00:00\n", + " 1.697197e+06\n", + " 189.028410\n", + " 1.697008e+06\n", + " 0.001499\n", + " 1.697197\n", " \n", " \n", - " 2019-12-26\n", - " 1.826130e+06\n", - " 57.640553\n", - " 1.826072e+06\n", - " 0.004588\n", - " 1.826130\n", + " 2019-12-26 00:00:00+00:00\n", + " 1.704983e+06\n", + " 189.028410\n", + " 1.704794e+06\n", + " 0.004587\n", + " 1.704983\n", " \n", " \n", - " 2019-12-27\n", - " 1.829028e+06\n", - " 57.640553\n", - " 1.828970e+06\n", + " 2019-12-27 00:00:00+00:00\n", + " 1.707689e+06\n", + " 189.028410\n", + " 1.707500e+06\n", " 0.001587\n", - " 1.829028\n", + " 1.707689\n", " \n", " \n", - " 2019-12-30\n", - " 1.824079e+06\n", - " 57.640553\n", - " 1.824021e+06\n", + " 2019-12-30 00:00:00+00:00\n", + " 1.703069e+06\n", + " 189.028410\n", + " 1.702880e+06\n", " -0.002706\n", - " 1.824079\n", + " 1.703069\n", " \n", " \n", - " 2019-12-31\n", - " 1.826055e+06\n", - " 57.640553\n", - " 1.825997e+06\n", + " 2019-12-31 00:00:00+00:00\n", + " 1.704913e+06\n", + " 189.028410\n", + " 1.704724e+06\n", " 0.001083\n", - " 1.826055\n", + " 1.704913\n", " \n", " \n", "\n", @@ -26259,31 +1977,275 @@ "" ], "text/plain": [ - " capital cash total_value % change \\\n", - "2010-01-03 1.000000e+06 1000000.000000 NaN NaN \n", - "2010-01-04 1.000000e+06 92.035288 9.999080e+05 0.000000 \n", - "2010-01-05 1.001321e+06 92.035288 1.001229e+06 0.001321 \n", - "2010-01-06 1.005620e+06 92.035288 1.005528e+06 0.004293 \n", - 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" accumulated return \n", - "2010-01-03 NaN \n", - "2010-01-04 1.000000 \n", - "2010-01-05 1.001321 \n", - "2010-01-06 1.005620 \n", - "2010-01-07 1.004723 \n", - "... ... \n", - "2019-12-24 1.817790 \n", - "2019-12-26 1.826130 \n", - "2019-12-27 1.829028 \n", - "2019-12-30 1.824079 \n", - "2019-12-31 1.826055 \n", + " % change accumulated return \n", + "2010-01-03 00:00:00+00:00 NaN NaN \n", + "2010-01-04 00:00:00+00:00 0.000000 1.000000 \n", + "2010-01-05 00:00:00+00:00 0.001321 1.001321 \n", + "2010-01-06 00:00:00+00:00 0.004276 1.005603 \n", + "2010-01-07 00:00:00+00:00 -0.000875 1.004723 \n", + "... ... ... \n", + "2019-12-24 00:00:00+00:00 0.001499 1.697197 \n", + "2019-12-26 00:00:00+00:00 0.004587 1.704983 \n", + "2019-12-27 00:00:00+00:00 0.001587 1.707689 \n", + "2019-12-30 00:00:00+00:00 -0.002706 1.703069 \n", + "2019-12-31 00:00:00+00:00 0.001083 1.704913 \n", + "\n", + "[2517 rows x 5 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Monthly rebalancing\n", + "bt.run(initial_capital=1_000_000, periods=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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..................
2019-12-24 00:00:00+00:001.766451e+06168.3622951.766283e+060.0014761.766451
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For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
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2010-01-031.000000e+061000000.000000NaNNaNNaN
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2010-01-051.001321e+0692.0352881.001229e+060.0013211.001321
2010-01-061.005620e+0692.0352881.005528e+060.0042931.005620
2010-01-071.004723e+0692.0352881.004631e+06-0.0008921.004723
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2019-12-241.823260e+06131.2090411.823129e+060.0014761.823260
2019-12-261.831628e+06131.2090411.831497e+060.0045901.831628
2019-12-271.834523e+06131.2090411.834392e+060.0015801.834523
2019-12-301.829496e+06131.2090411.829364e+06-0.0027401.829496
2019-12-311.831590e+06131.2090411.831458e+060.0011451.831590
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2517 rows × 5 columns

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1.000000e+06 1000000.000000 NaN NaN \n", - "2010-01-04 1.000000e+06 92.035288 9.999080e+05 0.000000 \n", - "2010-01-05 1.001321e+06 92.035288 1.001229e+06 0.001321 \n", - "2010-01-06 1.005620e+06 92.035288 1.005528e+06 0.004293 \n", - "2010-01-07 1.004723e+06 92.035288 1.004631e+06 -0.000892 \n", - "... ... ... ... ... \n", - "2019-12-24 1.823260e+06 131.209041 1.823129e+06 0.001476 \n", - "2019-12-26 1.831628e+06 131.209041 1.831497e+06 0.004590 \n", - "2019-12-27 1.834523e+06 131.209041 1.834392e+06 0.001580 \n", - "2019-12-30 1.829496e+06 131.209041 1.829364e+06 -0.002740 \n", - "2019-12-31 1.831590e+06 131.209041 1.831458e+06 0.001145 \n", - "\n", - " accumulated return \n", - "2010-01-03 NaN \n", - "2010-01-04 1.000000 \n", - "2010-01-05 1.001321 \n", - "2010-01-06 1.005620 \n", - "2010-01-07 1.004723 \n", - "... ... \n", - "2019-12-24 1.823260 \n", - "2019-12-26 1.831628 \n", - "2019-12-27 1.834523 \n", - "2019-12-30 1.829496 \n", - "2019-12-31 1.831590 \n", - "\n", - "[2517 rows x 5 columns]" + "alt.Chart(...)" ] }, "execution_count": 37, @@ -46796,38559 +2394,71 @@ "output_type": "execute_result" } ], - "source": [ - "# Bi-annual rebalancing\n", - "bt.run(initial_capital = 1_000_000, periods='6')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-2712e680b657d6064543d4d27d106cd3" - }, - "datasets": { - "data-2712e680b657d6064543d4d27d106cd3": [ - { - "% change": null, - "accumulated return": null, - "capital": 1000000, - "cash": 1000000, - "index": "2010-01-03T00:00:00", - "total_value": null - }, - { - "% change": 0, - "accumulated return": 1, - "capital": 1000000, - "cash": 92.03528785705566, - "index": "2010-01-04T00:00:00", - "total_value": 999907.964712143 - }, 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"scheme": "redyellowgreen" - }, - "title": "Return", - "type": "quantitative" - }, - "tooltip": { - "aggregate": "mean", - "field": "capital", - "format": ".2f", - "type": "quantitative" - }, - "x": { - "field": "date", - "timeUnit": "year", - "title": "Year", - "type": "ordinal" - }, - "y": { - "field": "date", - "timeUnit": "month", - "title": "Month", - "type": "ordinal" - } - }, - "mark": "rect", - "title": "Monthly Returns" - }, - "image/png": 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For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "monthly_returns_heatmap(bt.balance)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "application/vnd.vegalite.v3+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", - "config": { - "mark": { - "tooltip": null - }, - "view": { - "height": 300, - "width": 400 - } - }, - "data": { - "name": "data-1614661870b0d8230f3467026f315ffd" - }, - "datasets": { - "data-1614661870b0d8230f3467026f315ffd": [ - { - "% change": null, - "accumulated return": null, - "capital": 1000000, - "cash": 1000000, - "total_value": null - }, - { - "% change": 0, - "accumulated return": 1, - "capital": 1000000, - "cash": 92.03528785705566, - "total_value": 999907.964712143 - }, - { - "% change": 0.001320900440216155, - 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- "text/plain": [ - "\n", + "text/html": [ "\n", - "If you see this message, it means the renderer has not been properly enabled\n", - "for the frontend that you are using. For more information, see\n", - "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" ] }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -85381,7 +2491,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/asset_backtester/portfolio/asset.py b/asset_backtester/portfolio/asset.py index 2b85e9f..856a395 100644 --- a/asset_backtester/portfolio/asset.py +++ b/asset_backtester/portfolio/asset.py @@ -5,4 +5,4 @@ class Asset: self.percentage = percentage def __repr__(self): - return "Asset(symbol={}, percentage={}, direction={})".format(self.symbol, self.percentage, self.direction) + return "Asset(symbol={}, percentage={})".format(self.symbol, self.percentage) diff --git a/asset_backtester/statistics/__init__.py b/asset_backtester/statistics/__init__.py deleted file mode 100644 index ad4a5d6..0000000 --- a/asset_backtester/statistics/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .charts import returns_chart, returns_histogram, monthly_returns_heatmap diff --git a/asset_backtester/statistics/charts.py b/asset_backtester/statistics/charts.py deleted file mode 100644 index 4e9d2a2..0000000 --- a/asset_backtester/statistics/charts.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Generates charts from a portfolio report""" - -import altair as alt - - -def returns_chart(report): - # Time interval selector - time_interval = alt.selection(type='interval', encodings=['x']) - - # Area plot - areas = alt.Chart().mark_area(opacity=0.7).encode(x='index:T', - y=alt.Y('accumulated return:Q', axis=alt.Axis(format='%'))) - - # Nearest point selector - nearest = alt.selection(type='single', nearest=True, on='mouseover', fields=['index'], empty='none') - - points = areas.mark_point().encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0))) - - # Transparent date selector - selectors = alt.Chart().mark_point().encode( - x='index:T', - opacity=alt.value(0), - ).add_selection(nearest) - - text = areas.mark_text( - align='left', dx=5, - dy=-5).encode(text=alt.condition(nearest, 'accumulated return:Q', alt.value(' '), format='.2%')) - - layered = alt.layer(selectors, - points, - text, - areas.encode( - alt.X('index:T', axis=alt.Axis(title='date'), scale=alt.Scale(domain=time_interval))), - width=700, - height=350, - title='Wealth over time') - - lower = areas.properties(width=700, height=70).add_selection(time_interval) - - return alt.vconcat(layered, lower, data=report.reset_index()) - - -def returns_histogram(report): - bar = alt.Chart(report).mark_bar().encode(x=alt.X('% change:Q', - bin=alt.BinParams(maxbins=100), - axis=alt.Axis(format='%')), - y='count():Q') - return bar - - -def monthly_returns_heatmap(report): - resample = report.resample('M')['capital'].last() - monthly_returns = resample.pct_change().reset_index() - monthly_returns['capital'].iat[0] = resample.iloc[0] / report.iloc[0]['capital'] - 1 - monthly_returns.columns = ['date', 'capital'] - - chart = alt.Chart(monthly_returns).mark_rect().encode( - alt.X('year(date):O', title='Year'), alt.Y('month(date):O', title='Month'), - alt.Color('mean(capital)', title='Return', scale=alt.Scale(scheme='redyellowgreen')), - alt.Tooltip('mean(capital)', format='.2f')).properties(title='Monthly Returns') - - return chart