diff --git a/notebooks/lectures/From_Signal_To_Algorithm/notebook.ipynb b/notebooks/lectures/From_Signal_To_Algorithm/notebook.ipynb new file mode 100644 index 00000000..9f609222 --- /dev/null +++ b/notebooks/lectures/From_Signal_To_Algorithm/notebook.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# From Signal to Algorithm\n", + "by Christopher van Hoecke, Max Margenot and Delaney Granizo-Mackenzie\n", + "\n", + "After having run extensive research on your strategy using the research environment and alphalens, it is time to test your strategy in the backtest IDE. Backtesting is the process with which quantitative traders test their strategies after having found an alpha generating factor in the research environment, and tested its feasibility using alphalens. Once constructed, the backtest simulates a strategy’s performance based on relevant historic data, to ensure it performs as expected when taking real market parameters such as slippage and commissions into account. While backtesting can give us great insight into how our strategy would have performed, it is important to keep in mind that past performance is not indicative of future ones. Nevertheless, good backtesting practices helps in extracting a deeper understanding of how our strategy will react to market behaviors. In this lecture, we go over the steps towards constructing a successful backtest, and what to be cautious about. \n", + "\n", + "## Portfolio Construction\n", + "The portfolio construction is one of two things to look out for prior to launching a backtest. *We go over Best Code practices bellow.* Portfolio construction can be summarized by two main branches, optimizing the universe construction and position concentration.\n", + "\n", + "### Defining a trading universe\n", + "As described by Quantopian's CIO Jonathan Larkins in his [post](https://blog.quantopian.com/a-professional-quant-equity-workflow/) *A Professional Quant Equity Workflow*, the first step towards developing a successful trading strategy is defining a good universe. The reasons for this are multiple as explained in the post. However, we will only concern ourselves with thinking about the liquidity of an asset for now. Liquidity of an asset refers to how easily it can be converted to cash and how much of an impact the transaction will have on the asset's price. An asset on the NYSE that is illiquid is one that is not frequently traded, and who's price will therefore shift drastically in the event of a sale or purchase. Consequently, the presence of illiquid assets in your universe will have dire consequences on your trading strategy as the purchase and sale of these assets will spike the price up or down, increasing slippage, therefore increasing the percentage of returns being sacrificed in the transaction.\n", + "\n", + "You can protect against illiquidity in your universe by checking the volume associated with the assets in your trading. An asset with high volume is often exchanged, making it easier to find potential buyers and sellers for it. While you can construct your own liquid universe using the ` make_us_equity_universe`, picking from one of the predefined Q universes. *You can read more about both [here](https://www.quantopian.com/posts/the-q500us-and-q1500us)*. To illustrate the value of a good universe, we generate alphalens plots of the same momentum factor, during the same period, but with different universes (one uses the Q1500US, while the other does not filter its universe)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importing useful libraries\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from quantopian.pipeline import Pipeline\n", + "from quantopian.research import run_pipeline\n", + "from quantopian.pipeline.data.builtin import USEquityPricing\n", + "from quantopian.pipeline.factors import CustomFactor, Returns\n", + "from quantopian.pipeline.filters import Q1500US\n", + "from quantopian.pipeline.classifiers.fundamentals import Sector\n", + "\n", + "import alphalens as al\n", + "import pyfolio as pf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by constructing a CustomFactor. We choose to use the momentum factor, implemented in the Lecture Sample: Long-Short Equity, available [here](https://www.quantopian.com/lectures/example-long-short-equity-algorithm). We run pipeline twice, once using a screen to filter out assets that are not in the Q1500, and another with no screen. We then use `alphalens` to get the cumulative returns of our strategy in the period from 2015 to 2016. \n", + "*You can read about alphalens and factor analysis [here](https://www.quantopian.com/lectures/factor-analysis).*" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/numpy/lib/nanfunctions.py:1147: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", + " warnings.warn(\"Degrees of freedom <= 0 for slice.\", RuntimeWarning)\n" + ] + } + ], + "source": [ + "# Define a Momentum factor. \n", + "class Momentum(CustomFactor):\n", + " inputs = [USEquityPricing.close,\n", + " Returns(window_length=126)]\n", + " window_length = 252\n", + " def compute(self, today, assets, out, prices, returns):\n", + " out[:] = ((prices[-21] - prices[-252])/prices[-252] - \\\n", + " (prices[-1] - prices[-21])/prices[-21]) / np.nanstd(returns, axis=0)\n", + " \n", + "\n", + "# Running pipeline with and without the Q1500 universe screen. \n", + "with_universe = Pipeline(columns = {'Momentum ' : Momentum()}, screen = Q1500US())\n", + "without_universe = Pipeline(columns = {'Momentum' : Momentum()}, screen = ~Q1500US())\n", + "\n", + "with_universe_results = run_pipeline(with_universe, '2015-01-01', '2016-01-01')\n", + "without_universe_results = run_pipeline(without_universe, '2015-01-01', '2016-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Getting clean factor and forward returns for pipeline WITH Q1500 screen\n", + "with_clean = al.utils.get_clean_factor_and_forward_returns(\n", + " factor = with_universe_results, \n", + " prices = get_pricing(with_universe_results.index.levels[1],\n", + " start_date = '2015-01-01',\n", + " end_date = '2016-01-01',\n", + " fields = 'price'))\n", + "with_ls_factor_returns = al.performance.factor_returns(with_clean)\n", + "\n", + "# Getting clean factor and forward returns for pipeline WITHOUT Q1500 screen. \n", + "without_clean = al.utils.get_clean_factor_and_forward_returns(\n", + " factor = without_universe_results, \n", + " prices = get_pricing(without_universe_results.index.levels[1],\n", + " start_date = '2015-01-01',\n", + " end_date = '2016-01-01',\n", + " fields = 'price'))\n", + "without_ls_factor_returns = al.performance.factor_returns(without_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABCQAAAFwCAYAAABgsapPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGEEEKIB+7/AAkwEbDu9BQ+AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABB4AAAFwCAYAAAAfYLi+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting mean returns by factor Quantile for factor WITH Q1500 screen\n", + "al.plotting.plot_cumulative_returns(with_ls_factor_returns[1]);\n", + "\n", + "# Plotting mean returns by factor Quantile for Factor WITHOUT Q1500 screen. \n", + "al.plotting.plot_cumulative_returns(without_ls_factor_returns[1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these two outputs, we can see the importance of a good universe screen. *Note however that alphalens does **not consider slippage and commissions**. This means that the greater impact our transactions have on an asset's price are not considered in our returns. *\n", + "\n", + "### Long-Short Strategy\n", + "\n", + "One of the key methods towards being market independent is using a long-short strategy. This simply means that our strategy is at any given time searching for signals to both buy and short assets allowing us to significantly decrease our dependency to the market as we take positive action in the event of an expansion and a retraction. The neutrality of a long-short strategy mainly spawns from its use of a ranking scheme. After having developed a factor such as the momentum factor above we can rank factor values based on some criteria, and chose to long one basket while shorting another. We go over an example of this method bellow *If you'd like to read more about developing Long-Short equity strategies [here](https://www.quantopian.com/lectures/long-short-equity#notebook)* \n", + "\n", + "Bellow, we begin by constructing a Custom Sharpe ratio factor, defined as returns over volatility. And run pipeline to get the factor value for each asset in the Q1500 over the period from 2015 to 2016." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class Sharpe(CustomFactor):\n", + " inputs = [Returns(window_length = 2)]\n", + " window_length = 252\n", + " def compute(self, today, assets, out, returns):\n", + " out[:] = returns[0] / np.nanstd(returns, axis = 0)\n", + " \n", + "pipe = Pipeline(columns = {'Sharpe' : Sharpe()}, screen = Q1500US())\n", + "results = run_pipeline(pipe, '2015-01-01', '2016-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Sharpe
2015-01-02 00:00:00+00:00Equity(2 [ARNC])-0.501392
Equity(24 [AAPL])-1.038462
Equity(41 [ARCB])0.166821
Equity(62 [ABT])-0.269864
Equity(67 [ADSK])-1.280121
\n", + "
" + ], + "text/plain": [ + " Sharpe\n", + "2015-01-02 00:00:00+00:00 Equity(2 [ARNC]) -0.501392\n", + " Equity(24 [AAPL]) -1.038462\n", + " Equity(41 [ARCB]) 0.166821\n", + " Equity(62 [ABT]) -0.269864\n", + " Equity(67 [ADSK]) -1.280121" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here, we set about ranking our factor results. Assuming we would like to long assets with high Sharpe ratio, and short those with low Sharpe ratio, we can use pandas's `.rank` function to get the respective rank of each value. We then construct two universes: 'LONG', which consists of our factors top quartile, and one we shall call 'SHORT', which consists of our factors bottom quantile. This allows us to construct two sub-universes of assets to long, and short of equal sizes, which allows us to protect against market movements. *A simpler version of this would be to simply hedge beta (ie: Only short the benchmark, which in our case would be SPY, the S&P's ETF). You can read more about that in our lecture on [beta hedging](https://www.quantopian.com/lectures/beta-hedging).*" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of LONG: 92163\n", + "Length of SHORT: 92162\n" + ] + } + ], + "source": [ + "# ranking our factors into long and short bins. \n", + "results = results.rank()\n", + "top_quantil = results['Sharpe'].max() - (results['Sharpe'].max() * 25 / 100)\n", + "\n", + "LONG = results[results['Sharpe'] > top_quantil] # long top quantil\n", + "SHORT = results[results['Sharpe'] < (results['Sharpe'].max() * 25 / 100)] #short bottom quantil\n", + "\n", + "print 'Length of LONG:', len(LONG)\n", + "print 'Length of SHORT:', len(SHORT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sector Neutrality \n", + "\n", + "Alongside *beta neutrality* one of the key pillars of universe optimization involves being sector neutral. \n", + "Monitoring your sector exposure is especially important to protect against drawdown and volatility. Sectors are especially vulnerable to exterior influence such as political regime changes, economic bubbles and others. This is especially true during the 2008 subprime mortgage debt crises, the bio-tech, and oil bubble of 2016, or even the tech-bubble of 2001. Strategies that solely relied on these sectors to make profits incurred tremendous losses once the sellout began. Protecting against such unpredictable events is the key to having consistency in your strategy.\n", + "\n", + "There exists multiple way of protecting against sector dependencies, the simplest of which is using Pipeline's classifiers to make sure our universe is sector neutral. This can be done using pipeline as we show bellow. We use our Sharpe ratio factor from above and include Morningstar's sector classifier. This allows us to measure how well our factor performs in each sector and ensure we have a homogeneous performance throughout each sector. *You can read more about how to monitor for sectors using alphalens [here](https://www.quantopian.com/lectures/factor-analysis).*\n", + "\n", + "Below we run pipeline with our Sharpe factor, and Sector classifier from Januray to March 2015, and define our `MORNINGSTART_SECTOR_CODES` dictionary to assign a label to each sector code. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Defining and running pipeline\n", + "pipe = Pipeline(columns = {'Sharpe':Sharpe(),\n", + " 'Sectors' : Sector()},\n", + " screen = Q1500US())\n", + "\n", + "results = run_pipeline(pipe, '2015-01-01', '2015-05-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SectorsSharpe
2015-01-02 00:00:00+00:00Equity(2 [ARNC])101-0.501392
Equity(24 [AAPL])311-1.038462
Equity(41 [ARCB])3100.166821
Equity(62 [ABT])206-0.269864
Equity(67 [ADSK])311-1.280121
\n", + "
" + ], + "text/plain": [ + " Sectors Sharpe\n", + "2015-01-02 00:00:00+00:00 Equity(2 [ARNC]) 101 -0.501392\n", + " Equity(24 [AAPL]) 311 -1.038462\n", + " Equity(41 [ARCB]) 310 0.166821\n", + " Equity(62 [ABT]) 206 -0.269864\n", + " Equity(67 [ADSK]) 311 -1.280121" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sector labels \n", + "MORNINGSTAR_SECTOR_CODES = {\n", + " -1: 'Misc',\n", + " 101: 'Basic Materials',\n", + " 102: 'Consumer Cyclical',\n", + " 103: 'Financial Services',\n", + " 104: 'Real Estate',\n", + " 205: 'Consumer Defensive',\n", + " 206: 'Healthcare',\n", + " 207: 'Utilities',\n", + " 308: 'Communication Services',\n", + " 309: 'Energy',\n", + " 310: 'Industrials',\n", + " 311: 'Technology' , \n", + "}\n", + "\n", + "results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To analyse our factor, we use `alphalens`'s `get_clean_factor_and_forward_returns` function to get our factor_data by adding the `groupby` and `groupby_labels`, to assign a sector to each value. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Defining factor_data with groupby\n", + "factor_data = al.utils.get_clean_factor_and_forward_returns(\n", + " factor=results['Sharpe'],\n", + " prices = get_pricing(results.index.levels[1].unique(),\n", + " start_date = '2015-01-01',\n", + " end_date = '2015-05-01',\n", + " fields = 'price'),\n", + " groupby=results['Sectors'],\n", + " groupby_labels=MORNINGSTAR_SECTOR_CODES\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use our `factor_data` to get our mean returns by sector, and plot them using the `plot quantile_returns_bar`. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1510
factor_quantilegroup
1Basic Materials-0.000929-0.006150-0.003587
Communication Services-0.0001090.0032270.007938
Consumer Cyclical-0.001115-0.001664-0.002345
Consumer Defensive-0.000619-0.001283-0.003945
Energy-0.0005140.0041120.010247
\n", + "
" + ], + "text/plain": [ + " 1 5 10\n", + "factor_quantile group \n", + "1 Basic Materials -0.000929 -0.006150 -0.003587\n", + " Communication Services -0.000109 0.003227 0.007938\n", + " Consumer Cyclical -0.001115 -0.001664 -0.002345\n", + " Consumer Defensive -0.000619 -0.001283 -0.003945\n", + " Energy -0.000514 0.004112 0.010247" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sector mean returns \n", + "mean_return_quantile_sector, mean_return_quantile_sector_err = (\n", + " al.performance.mean_return_by_quantile(factor_data,\n", + " by_group=True))\n", + "\n", + "mean_return_quantile_sector.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABCEAAAgBCAYAAADTfvq8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAJPH/Aw6TTrbC9ADHAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "al.plotting.plot_quantile_returns_bar(mean_return_quantile_sector.apply(al.utils.rate_of_return, axis=0), by_group=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using alphalens's `groupby` parameter we get the quantile returns grouped by sector. This allows us to see how our signal performs throughout sectors. We should use this to check for consistency throughout each sector, making sure our returns are not fueled solely by a few sectors. We can repeat the same process with the `Information Coefficient` to assess the predictive power of our factor across different sectors, again looking for homogeneity throughout each sector. You can read more about finding sector specific IC [here](https://www.quantopian.com/lectures/factor-analysis).\n", + "\n", + "### Position Concentration\n", + "\n", + "A key concept in finance is that of diversification. By assigning more weights on uncorrelated assets, we can simulate placing independent bets, which allows us to hedge out risk. *You can read more about position concentration [here](https://www.quantopian.com/lectures/position-concentration-risk).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code practices\n", + "As mentioned above, the second important aspect of signal development is coding. A well written algorithm is one where we are aware of what is going on at all moments, and will not behave unexpectedly in the event of an anomaly. Bellow we go over a few practices to think of when developing your algorithm to make sure we're constantly aware of what's going on. To do this we split this section into two sub-sections that deal with safeguards, and precautious methods.\n", + "\n", + "### Ordering\n", + "Rebalancing is one of the key moments of the backtest as it is where the buying and selling occurs. Making sure that everything runs smoothly at this stage is vital. Other than screening pipeline to filter assets in the Q1500, using functions like ```data.can_trade``` in the backtest IDE ensures we only trade assets that have not been dropped from the stock exchange. To prevent against this and ensure our strategy orders assets on a more holistic form, use the Optimize API to ensure the transactions are executed in the most effective of ways to reduce the strategies market impact, slippage and commissions. *You can read more about the order optimal API [here](https://www.quantopian.com/help#optimize-api)*\n", + "\n", + "Below we go over an example of `order_optimal_portfolio`, which uses the `TargetPortfolioWeights` class which, given a desired weight will attempt to find the optimal weights associated with each asset. In our example we assign equal weights to each asset, and use the `MaxGrossLeverage` constraint to set a maximum leverage our strategy can take *(in this case it is 1x)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from quantopian.experimental import optimize as opt\n", + "\n", + "# Assign desired weights in the form of a dictionary or a pandas Series. \n", + "weights = {'AAPL':0.5, \n", + " 'SBUX':0.5}\n", + "\n", + "obj = opt.TargetPortfolioWeights(weights)\n", + "const = opt.MaxGrossLeverage(1)\n", + "opt.order_optimal_portfolio(objective = obj, \n", + " constraints = const)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logging\n", + "The more data we can collect on our backtest, the better we can anticipate how it will perform out of sample. To do this, recording variables becomes a necessity as it allows quickly view what assets our algorithm is longing and shorting, but also useful parameters that would prove to be useful in the analysis process of our backtest. We can easily log data using the `record_vars` function. Below we have a snippet of code from the Sample Mean Reversion algorithm. *You can read more about plotting and recording variables [here](https://www.quantopian.com/help#ide-record)*. \n", + "\n", + "Below we go over an example of the `record_vars` function, which will simply add a count for each asset in `context.portfolio.positions`, a method that outputs all the assets in our portfolio. The `record` function below records and plots the leverage, as well as the long and short counts that will be output alongside our backtest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def record_vars(context, data):\n", + " \"\"\"\n", + " This function is called at the end of each day and plots certain variables.\n", + " \"\"\"\n", + "\n", + " # Check how many long and short positions we have.\n", + " longs = shorts = 0\n", + " for position in context.portfolio.positions.itervalues():\n", + " if position.amount > 0:\n", + " longs += 1\n", + " if position.amount < 0:\n", + " shorts += 1\n", + "\n", + " # Record and plot the leverage of our portfolio over time as well as the\n", + " # number of long and short positions. Even in minute mode, only the end-of-day\n", + " # leverage is plotted.\n", + " record(leverage = context.account.leverage, long_count=longs, short_count=shorts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rebalancing\n", + "\n", + "Rebalancing is the process of adjusting a portfolio's weights (ie: selling and buying assets). One of the most crucial tasks when constructing a backtest is picking the ideal rebalancing period, ie: how often our strategy actually buys and sells assets. A strategy that rebalances too often incurs higher transaction costs compared to one that does so on a monthly basis for instance. When running your backtest it is important to consider how often your algorithm rebalances, or you might just drain all your returns.\n", + "\n", + "Bellow we use the Sample Mean Reversion Algorithm in the algorithm [page](https://www.quantopian.com/algorithms). We ran two seperate backtests, once without changing anything and a second time changing the date_rules from `week_start` to `every_day` parameter in the `schedule_function` associated with the `rebalance` function. This changes how often our strategy will rebalance from every start of the week to every day. You can read more about the scheduling function [here](https://www.quantopian.com/tutorials/getting-started#lesson7). From here we plot the daily `ending_portfolio_value` during the backtest period. This gives us a snapshot of how our two strategies performe over time." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100% Time: 0:03:19|###########################################################|\n", + "100% Time: 0:04:26|###########################################################|\n" + ] + } + ], + "source": [ + "week_start = get_backtest('59922d1a7b987650e72035d0')\n", + "every_day = get_backtest('59922df8ba61b04fbd7cd3f6')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzYAAAHrCAYAAAAdcAiZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIHN7/B/a1AcbsA9SfAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting ending portfolio value for weekly and daily rebalancing\n", + "plt.plot(week_start.daily_performance['ending_portfolio_value'], label = 'Weekly Rebalancing')\n", + "plt.plot(every_day.daily_performance['ending_portfolio_value'], label = 'Daily Rebalancing')\n", + "plt.title('Plot of ending portfolio value for Weekly and Daily Rebalancing')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the graph above, we can clearly see the benefits of rebalancing less often. By subtracting the last two `ending portfolio values`, we can quantify the impact rebalancing too often has on our strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference in portfolio values between weekly and daily rebalancing: 990448.360435 $\n", + "Percentage of base capital: 10.0 %\n" + ] + } + ], + "source": [ + "# Difference \n", + "difference = (week_start.daily_performance['ending_portfolio_value'][-1]) - \\\n", + " (every_day.daily_performance['ending_portfolio_value'][-1])\n", + "print 'Difference in portfolio values between weekly and daily rebalancing:', difference, '$'\n", + "\n", + "# percentage difference\n", + "percentage_difference = difference * 100 / week_start.capital_base\n", + "print 'Percentage of base capital:', round(percentage_difference), '%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changing the rebalancing period of our strategy leads to a 10% decrease in returns, eaten up by transaction costs. Optimizing the rebalance window is not solely motivated by increasing transaction costs however. Each strategy has it's innate optimal rebalance window, which should be found using economic rationale. We opt for using economic rationale as opposed to some optimization technique as it is far too easy to succumb to overfitting, discussed in another section. *Using quarterly rebalancing for a strategy that involved earning reports, or monthly rebalancing for a strategy that uses rolling monthly averages, or even daily rebalancing for a strategy that uses weather forecasting as a trading signal are good examples of 'innate' rebalancing periods.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Other things to look out for\n", + "While the practices mentioned in this notebook will help protect against, or help expose unwanted behaviors, being fully protected remains a challenging task. Here are a few more things to look out for when developing your signal.\n", + "\n", + "- **Not using python exceptions which lead to unknown behaviors. ** \n", + "*An algorithm that uses a `try`/`except` loop can lead to unexpected behaviors in your algorithm as the loop will simply pass over an error. It is more beneficial in the long run for you to be aware of the error that is being brought up by the algorithm that to ignore it*. \n", + "- **When possible, constructing a stateless algorithm is preferred**. \n", + "*A stateless strategy is one that does not consider past performance to make future decisions. This is an advantage as it allows us to protect against data glitches and overfitting, not to mention it is easier in terms of computation. *\n", + "- **Using alternative datasets. ** \n", + "*Using alternative datasets helps hedging out against common risk factors. Synonymous with 'Drained Alpha Factor', a common risk factor is one that has already been priced into the market due to the large number of traders using it. You can protect against this by construct your strategies using alternative data available [here](https://www.quantopian.com/data), note that all datasets can be used for free over a certain time window. *\n", + "\n", + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/lectures/From_Signal_To_Algorithm/preview.html b/notebooks/lectures/From_Signal_To_Algorithm/preview.html new file mode 100644 index 00000000..07ad92cb --- /dev/null +++ b/notebooks/lectures/From_Signal_To_Algorithm/preview.html @@ -0,0 +1,16835 @@ + + + From Signal to Algorithm Draft I + + + + + + + + + + + + + + + + +
+
+ +
+
+
+
+

From Signal to Algorithm¶

by Christopher van Hoecke, Max Margenot and Delaney Granizo-Mackenzie

+

After having run extensive research on your strategy using the research environment and alphalens, it is time to test your strategy in the backtest IDE. Backtesting is the process with which quantitative traders test their strategies after having found an alpha generating factor in the research environment, and tested its feasibility using alphalens. Once constructed, the backtest simulates a strategy’s performance based on relevant historic data, to ensure it performs as expected when taking real market parameters such as slippage and commissions into account. While backtesting can give us great insight into how our strategy would have performed, it is important to keep in mind that past performance is not indicative of future ones. Nevertheless, good backtesting practices helps in extracting a deeper understanding of how our strategy will react to market behaviors. In this lecture, we go over the steps towards constructing a successful backtest, and what to be cautious about.

+

Portfolio Construction¶

The portfolio construction is one of two things to look out for prior to launching a backtest. We go over Best Code practices bellow. Portfolio construction can be summarized by two main branches, optimizing the universe construction and position concentration.

+

Defining a trading universe¶

As described by Quantopian's CIO Jonathan Larkins in his post A Professional Quant Equity Workflow, the first step towards developing a successful trading strategy is defining a good universe. The reasons for this are multiple as explained in the post. However, we will only concern ourselves with thinking about the liquidity of an asset for now. Liquidity of an asset refers to how easily it can be converted to cash and how much of an impact the transaction will have on the asset's price. An asset on the NYSE that is illiquid is one that is not frequently traded, and who's price will therefore shift drastically in the event of a sale or purchase. Consequently, the presence of illiquid assets in your universe will have dire consequences on your trading strategy as the purchase and sale of these assets will spike the price up or down, increasing slippage, therefore increasing the percentage of returns being sacrificed in the transaction.

+

You can protect against illiquidity in your universe by checking the volume associated with the assets in your trading. An asset with high volume is often exchanged, making it easier to find potential buyers and sellers for it. While you can construct your own liquid universe using the make_us_equity_universe, picking from one of the predefined Q universes. You can read more about both here. To illustrate the value of a good universe, we generate alphalens plots of the same momentum factor, during the same period, but with different universes (one uses the Q1500US, while the other does not filter its universe).

+ +
+
+
+
+
+
In [2]:
+
+
+
# Importing useful libraries
+import matplotlib.pyplot as plt
+import numpy as np
+
+from quantopian.pipeline import Pipeline
+from quantopian.research import run_pipeline
+from quantopian.pipeline.data.builtin import USEquityPricing
+from quantopian.pipeline.factors import CustomFactor, Returns
+from quantopian.pipeline.filters import Q1500US
+from quantopian.pipeline.classifiers.fundamentals import Sector
+
+import alphalens as al
+import pyfolio as pf
+
+ +
+
+
+ +
+
+
+
+
+

We begin by constructing a CustomFactor. We choose to use the momentum factor, implemented in the Lecture Sample: Long-Short Equity, available here. We run pipeline twice, once using a screen to filter out assets that are not in the Q1500, and another with no screen. We then use alphalens to get the cumulative returns of our strategy in the period from 2015 to 2016.
+You can read about alphalens and factor analysis here.

+ +
+
+
+
+
+
In [3]:
+
+
+
# Define a Momentum factor. 
+class Momentum(CustomFactor):
+        inputs = [USEquityPricing.close,
+                  Returns(window_length=126)]
+        window_length = 252
+        def compute(self, today, assets, out, prices, returns):
+            out[:] = ((prices[-21] - prices[-252])/prices[-252] - \
+                      (prices[-1] - prices[-21])/prices[-21]) / np.nanstd(returns, axis=0)
+        
+
+# Running pipeline with and without the Q1500 universe screen. 
+with_universe = Pipeline(columns = {'Momentum ' : Momentum()}, screen = Q1500US())
+without_universe = Pipeline(columns = {'Momentum' : Momentum()}, screen = ~Q1500US())
+
+with_universe_results = run_pipeline(with_universe, '2015-01-01', '2016-01-01')
+without_universe_results = run_pipeline(without_universe, '2015-01-01', '2016-01-01')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/usr/local/lib/python2.7/dist-packages/numpy/lib/nanfunctions.py:1147: RuntimeWarning: Degrees of freedom <= 0 for slice.
+  warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning)
+
+
+
+ +
+
+ +
+
+
+
In [4]:
+
+
+
# Getting clean factor and forward returns for pipeline WITH Q1500 screen
+with_clean = al.utils.get_clean_factor_and_forward_returns(
+                                             factor = with_universe_results, 
+                                             prices = get_pricing(with_universe_results.index.levels[1],
+                                                                 start_date = '2015-01-01',
+                                                                 end_date = '2016-01-01',
+                                                                  fields = 'price'))
+with_ls_factor_returns = al.performance.factor_returns(with_clean)
+
+# Getting clean factor and forward returns for pipeline WITHOUT Q1500 screen. 
+without_clean = al.utils.get_clean_factor_and_forward_returns(
+                                             factor = without_universe_results, 
+                                             prices = get_pricing(without_universe_results.index.levels[1],
+                                                                 start_date = '2015-01-01',
+                                                                 end_date = '2016-01-01',
+                                                                  fields = 'price'))
+without_ls_factor_returns = al.performance.factor_returns(without_clean)
+
+ +
+
+
+ +
+
+
+
In [5]:
+
+
+
# Plotting mean returns by factor Quantile for factor WITH Q1500 screen
+al.plotting.plot_cumulative_returns(with_ls_factor_returns[1]);
+
+# Plotting mean returns by factor Quantile for Factor WITHOUT Q1500 screen. 
+al.plotting.plot_cumulative_returns(without_ls_factor_returns[1]);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

From these two outputs, we can see the importance of a good universe screen. Note however that alphalens does not consider slippage and commissions. This means that the greater impact our transactions have on an asset's price are not considered in our returns.

+

Long-Short Strategy¶

One of the key methods towards being market independent is using a long-short strategy. This simply means that our strategy is at any given time searching for signals to both buy and short assets allowing us to significantly decrease our dependency to the market as we take positive action in the event of an expansion and a retraction. The neutrality of a long-short strategy mainly spawns from its use of a ranking scheme. After having developed a factor such as the momentum factor above we can rank factor values based on some criteria, and chose to long one basket while shorting another. We go over an example of this method bellow If you'd like to read more about developing Long-Short equity strategies here

+

Bellow, we begin by constructing a Custom Sharpe ratio factor, defined as returns over volatility. And run pipeline to get the factor value for each asset in the Q1500 over the period from 2015 to 2016.

+ +
+
+
+
+
+
In [6]:
+
+
+
class Sharpe(CustomFactor):
+    inputs = [Returns(window_length = 2)]
+    window_length = 252
+    def compute(self, today, assets, out, returns):
+        out[:] = returns[0] / np.nanstd(returns, axis = 0)
+        
+pipe = Pipeline(columns = {'Sharpe' : Sharpe()}, screen = Q1500US())
+results = run_pipeline(pipe, '2015-01-01', '2016-01-01')
+
+ +
+
+
+ +
+
+
+
In [7]:
+
+
+
results.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[7]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Sharpe
2015-01-02 00:00:00+00:00Equity(2 [ARNC])-0.501392
Equity(24 [AAPL])-1.038462
Equity(41 [ARCB])0.166821
Equity(62 [ABT])-0.269864
Equity(67 [ADSK])-1.280121
+
+
+ +
+ +
+
+ +
+
+
+
+
+

From here, we set about ranking our factor results. Assuming we would like to long assets with high Sharpe ratio, and short those with low Sharpe ratio, we can use pandas's .rank function to get the respective rank of each value. We then construct two universes: 'LONG', which consists of our factors top quartile, and one we shall call 'SHORT', which consists of our factors bottom quantile. This allows us to construct two sub-universes of assets to long, and short of equal sizes, which allows us to protect against market movements. A simpler version of this would be to simply hedge beta (ie: Only short the benchmark, which in our case would be SPY, the S&P's ETF). You can read more about that in our lecture on beta hedging.

+ +
+
+
+
+
+
In [8]:
+
+
+
# ranking our factors into long and short bins. 
+results = results.rank()
+top_quantil = results['Sharpe'].max() - (results['Sharpe'].max() * 25 / 100)
+
+LONG = results[results['Sharpe'] > top_quantil] # long top quantil
+SHORT = results[results['Sharpe'] < (results['Sharpe'].max() * 25 / 100)] #short bottom quantil
+
+print 'Length of LONG:', len(LONG)
+print 'Length of SHORT:', len(SHORT)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Length of LONG: 92163
+Length of SHORT: 92162
+
+
+
+ +
+
+ +
+
+
+
+
+

Sector Neutrality¶

Alongside beta neutrality one of the key pillars of universe optimization involves being sector neutral. +Monitoring your sector exposure is especially important to protect against drawdown and volatility. Sectors are especially vulnerable to exterior influence such as political regime changes, economic bubbles and others. This is especially true during the 2008 subprime mortgage debt crises, the bio-tech, and oil bubble of 2016, or even the tech-bubble of 2001. Strategies that solely relied on these sectors to make profits incurred tremendous losses once the sellout began. Protecting against such unpredictable events is the key to having consistency in your strategy.

+

There exists multiple way of protecting against sector dependencies, the simplest of which is using Pipeline's classifiers to make sure our universe is sector neutral. This can be done using pipeline as we show bellow. We use our Sharpe ratio factor from above and include Morningstar's sector classifier. This allows us to measure how well our factor performs in each sector and ensure we have a homogeneous performance throughout each sector. You can read more about how to monitor for sectors using alphalens here.

+

Below we run pipeline with our Sharpe factor, and Sector classifier from Januray to March 2015, and define our MORNINGSTART_SECTOR_CODES dictionary to assign a label to each sector code.

+ +
+
+
+
+
+
In [9]:
+
+
+
# Defining and running pipeline
+pipe = Pipeline(columns = {'Sharpe':Sharpe(),
+                           'Sectors' : Sector()},
+               screen = Q1500US())
+
+results = run_pipeline(pipe, '2015-01-01', '2015-05-01')
+
+ +
+
+
+ +
+
+
+
In [10]:
+
+
+
# Sector labels 
+MORNINGSTAR_SECTOR_CODES = {
+     -1: 'Misc',
+    101: 'Basic Materials',
+    102: 'Consumer Cyclical',
+    103: 'Financial Services',
+    104: 'Real Estate',
+    205: 'Consumer Defensive',
+    206: 'Healthcare',
+    207: 'Utilities',
+    308: 'Communication Services',
+    309: 'Energy',
+    310: 'Industrials',
+    311: 'Technology' ,    
+}
+
+results.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[10]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SectorsSharpe
2015-01-02 00:00:00+00:00Equity(2 [ARNC])101-0.501392
Equity(24 [AAPL])311-1.038462
Equity(41 [ARCB])3100.166821
Equity(62 [ABT])206-0.269864
Equity(67 [ADSK])311-1.280121
+
+
+ +
+ +
+
+ +
+
+
+
+
+

To analyse our factor, we use alphalens's get_clean_factor_and_forward_returns function to get our factor_data by adding the groupby and groupby_labels, to assign a sector to each value.

+ +
+
+
+
+
+
In [11]:
+
+
+
# Defining factor_data with groupby
+factor_data = al.utils.get_clean_factor_and_forward_returns(
+                                                    factor=results['Sharpe'],
+                                                    prices = get_pricing(results.index.levels[1].unique(),
+                                                                           start_date = '2015-01-01',
+                                                                           end_date = '2015-05-01',
+                                                                           fields = 'price'),
+                                                    groupby=results['Sectors'],
+                                                    groupby_labels=MORNINGSTAR_SECTOR_CODES
+                                                           )
+
+ +
+
+
+ +
+
+
+
+
+

We can now use our factor_data to get our mean returns by sector, and plot them using the plot quantile_returns_bar.

+ +
+
+
+
+
+
In [12]:
+
+
+
# Sector mean returns 
+mean_return_quantile_sector, mean_return_quantile_sector_err = (
+                                al.performance.mean_return_by_quantile(factor_data,
+                                                                       by_group=True))
+
+mean_return_quantile_sector.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[12]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
1510
factor_quantilegroup
1Basic Materials-0.000929-0.006150-0.003587
Communication Services-0.0001090.0032270.007938
Consumer Cyclical-0.001115-0.001664-0.002345
Consumer Defensive-0.000619-0.001283-0.003945
Energy-0.0005140.0041120.010247
+
+
+ +
+ +
+
+ +
+
+
+
In [13]:
+
+
+
al.plotting.plot_quantile_returns_bar(mean_return_quantile_sector.apply(al.utils.rate_of_return, axis=0), by_group=True);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Using alphalens's groupby parameter we get the quantile returns grouped by sector. This allows us to see how our signal performs throughout sectors. We should use this to check for consistency throughout each sector, making sure our returns are not fueled solely by a few sectors. We can repeat the same process with the Information Coefficient to assess the predictive power of our factor across different sectors, again looking for homogeneity throughout each sector. You can read more about finding sector specific IC here.

+

Position Concentration¶

A key concept in finance is that of diversification. By assigning more weights on uncorrelated assets, we can simulate placing independent bets, which allows us to hedge out risk. You can read more about position concentration here.

+ +
+
+
+
+
+
+
+

Code practices¶

As mentioned above, the second important aspect of signal development is coding. A well written algorithm is one where we are aware of what is going on at all moments, and will not behave unexpectedly in the event of an anomaly. Bellow we go over a few practices to think of when developing your algorithm to make sure we're constantly aware of what's going on. To do this we split this section into two sub-sections that deal with safeguards, and precautious methods.

+

Ordering¶

Rebalancing is one of the key moments of the backtest as it is where the buying and selling occurs. Making sure that everything runs smoothly at this stage is vital. Other than screening pipeline to filter assets in the Q1500, using functions like data.can_trade in the backtest IDE ensures we only trade assets that have not been dropped from the stock exchange. To prevent against this and ensure our strategy orders assets on a more holistic form, use the Optimize API to ensure the transactions are executed in the most effective of ways to reduce the strategies market impact, slippage and commissions. You can read more about the order optimal API here

+

Below we go over an example of order_optimal_portfolio, which uses the TargetPortfolioWeights class which, given a desired weight will attempt to find the optimal weights associated with each asset. In our example we assign equal weights to each asset, and use the MaxGrossLeverage constraint to set a maximum leverage our strategy can take (in this case it is 1x)

+ +
+
+
+
+
+
In [ ]:
+
+
+
from quantopian.experimental import optimize as opt
+
+# Assign desired weights in the form of a dictionary or a pandas Series. 
+weights = {'AAPL':0.5, 
+           'SBUX':0.5}
+
+obj = opt.TargetPortfolioWeights(weights)
+const = opt.MaxGrossLeverage(1)
+opt.order_optimal_portfolio(objective = obj, 
+                        constraints = const)
+
+ +
+
+
+ +
+
+
+
+
+

Logging¶

The more data we can collect on our backtest, the better we can anticipate how it will perform out of sample. To do this, recording variables becomes a necessity as it allows quickly view what assets our algorithm is longing and shorting, but also useful parameters that would prove to be useful in the analysis process of our backtest. We can easily log data using the record_vars function. Below we have a snippet of code from the Sample Mean Reversion algorithm. You can read more about plotting and recording variables here.

+

Below we go over an example of the record_vars function, which will simply add a count for each asset in context.portfolio.positions, a method that outputs all the assets in our portfolio. The record function below records and plots the leverage, as well as the long and short counts that will be output alongside our backtest.

+ +
+
+
+
+
+
In [ ]:
+
+
+
def record_vars(context, data):
+    """
+    This function is called at the end of each day and plots certain variables.
+    """
+
+    # Check how many long and short positions we have.
+    longs = shorts = 0
+    for position in context.portfolio.positions.itervalues():
+        if position.amount > 0:
+            longs += 1
+        if position.amount < 0:
+            shorts += 1
+
+    # Record and plot the leverage of our portfolio over time as well as the
+    # number of long and short positions. Even in minute mode, only the end-of-day
+    # leverage is plotted.
+    record(leverage = context.account.leverage, long_count=longs, short_count=shorts)
+
+ +
+
+
+ +
+
+
+
+
+

Rebalancing¶

Rebalancing is the process of adjusting a portfolio's weights (ie: selling and buying assets). One of the most crucial tasks when constructing a backtest is picking the ideal rebalancing period, ie: how often our strategy actually buys and sells assets. A strategy that rebalances too often incurs higher transaction costs compared to one that does so on a monthly basis for instance. When running your backtest it is important to consider how often your algorithm rebalances, or you might just drain all your returns.

+

Bellow we use the Sample Mean Reversion Algorithm in the algorithm page. We ran two seperate backtests, once without changing anything and a second time changing the date_rules from week_start to every_day parameter in the schedule_function associated with the rebalance function. This changes how often our strategy will rebalance from every start of the week to every day. You can read more about the scheduling function here. From here we plot the daily ending_portfolio_value during the backtest period. This gives us a snapshot of how our two strategies performe over time.

+ +
+
+
+
+
+
In [15]:
+
+
+
week_start = get_backtest('59922d1a7b987650e72035d0')
+every_day = get_backtest('59922df8ba61b04fbd7cd3f6')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
100% Time: 0:03:19|###########################################################|
+100% Time: 0:04:26|###########################################################|
+
+
+
+ +
+
+ +
+
+
+
In [16]:
+
+
+
# Plotting ending portfolio value for weekly and daily rebalancing
+plt.plot(week_start.daily_performance['ending_portfolio_value'], label = 'Weekly Rebalancing')
+plt.plot(every_day.daily_performance['ending_portfolio_value'], label = 'Daily Rebalancing')
+plt.title('Plot of ending portfolio value for Weekly and Daily Rebalancing')
+plt.legend();
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

From the graph above, we can clearly see the benefits of rebalancing less often. By subtracting the last two ending portfolio values, we can quantify the impact rebalancing too often has on our strategy.

+ +
+
+
+
+
+
In [17]:
+
+
+
# Difference 
+difference = (week_start.daily_performance['ending_portfolio_value'][-1]) - \
+             (every_day.daily_performance['ending_portfolio_value'][-1])
+print 'Difference in portfolio values between weekly and daily rebalancing:', difference, '$'
+
+# percentage difference
+percentage_difference = difference * 100 / week_start.capital_base
+print 'Percentage of base capital:', round(percentage_difference), '%'
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Difference in portfolio values between weekly and daily rebalancing: 990448.360435 $
+Percentage of base capital: 10.0 %
+
+
+
+ +
+
+ +
+
+
+
+
+

Changing the rebalancing period of our strategy leads to a 10% decrease in returns, eaten up by transaction costs. Optimizing the rebalance window is not solely motivated by increasing transaction costs however. Each strategy has it's innate optimal rebalance window, which should be found using economic rationale. We opt for using economic rationale as opposed to some optimization technique as it is far too easy to succumb to overfitting, discussed in another section. Using quarterly rebalancing for a strategy that involved earning reports, or monthly rebalancing for a strategy that uses rolling monthly averages, or even daily rebalancing for a strategy that uses weather forecasting as a trading signal are good examples of 'innate' rebalancing periods.

+ +
+
+
+
+
+
+
+

Other things to look out for¶

While the practices mentioned in this notebook will help protect against, or help expose unwanted behaviors, being fully protected remains a challenging task. Here are a few more things to look out for when developing your signal.

+
    +
  • Not using python exceptions which lead to unknown behaviors.
    +An algorithm that uses a try/except loop can lead to unexpected behaviors in your algorithm as the loop will simply pass over an error. It is more beneficial in the long run for you to be aware of the error that is being brought up by the algorithm that to ignore it.
    • +
  • When possible, constructing a stateless algorithm is preferred.
    +A stateless strategy is one that does not consider past performance to make future decisions. This is an advantage as it allows us to protect against data glitches and overfitting, not to mention it is easier in terms of computation.
    • +
  • Using alternative datasets.
    +Using alternative datasets helps hedging out against common risk factors. Synonymous with 'Drained Alpha Factor', a common risk factor is one that has already been priced into the market due to the large number of traders using it. You can protect against this by construct your strategies using alternative data available here, note that all datasets can be used for free over a certain time window.
    • +
+
+ +
+
+
+
+
+
+
+

This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. ("Quantopian"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.

+ +
+
+
+
+
+