order_target_size ( d, size )Īs we can see in the code, the strategy looks for stocks it needs to sell every week in the rebalance_portfolio method and rebalances all of its positions every other week in the rebalance_positions method.After a trader opens a long or short position by placing an order with their broker, they will often add a follow-up stop-loss order to limit the amount of money they can lose if the investment moves against them. get_value () if cash <= 0 : break size = value * 0.001 / self. spy_sma200 : return # rebalance all stocksįor i, d in enumerate ( self. buy ( d, size = size ) def rebalance_positions ( self ): num_stocks = len ( self. get_value () if cash <= 0 : break if not self. spy_sma200 : return # buy stocks with remaining cashįor i, d in enumerate ( self. size : if i > num_stocks * 0.2 or d < self. rankings ) # sell stocks based on criteriaįor i, d in enumerate ( self. rankings = list ( filter ( lambda d : len ( d ) > 100, self.

i += 1 def rebalance_portfolio ( self ): # only look at data that we can have indicators for ATR ( d, period = 20 ) def prenext ( self ): # call next() even when data is not available for all tickers Each stock is assigned a size using the following formula: Risk Parity SizingĬlenow’s strategy uses risk parity allocation to calculate the position sizes of each stock. e ** ( intercept + slope * x ))Īs we can see, the regression curves fit each stock pretty well The stocks do not seem to follow the curve outside of the measurement window, but it is important to remember that this momentum indicator is only used for ranking the stocks, and is in no way trying to predict prices. arange ( len ( rets )) slope, intercept, r_value, p_value, std_err = linregress ( x, rets ) plt. index for best in bests : end = momentums. ylabel ( 'Stock Price' ) bests = momentums. To see this in action, let’s look at the highest momentum values measured in our dataset. MomentumĪs mentioned above, momentum is calculated by multiplying the annualized exponential regression slope of the past 90 days by the $R^2$ coefficient of the regression calculation.

Buy stocks in the top 20% momentum rankings with remaining cash.Įvery other week, rebalance existing positions with updated Average True Range values.īefore we backtest the strategy, let’s look into the momentum and position size formulas. Only open new positions if the S&P 500 is above its 200-day moving average.Įvery week, look to sell stocks that are not in the top 20% momentum ranking, or have fallen below their 100 day moving average. Position size is calculated using the 20-day Average True Range of each stock, multiplied by 10 basis points of the portfolio value. Momentum is calculated by multiplying the annualized exponential regression slope of the past 90 days by the $R^2$ coefficient of the regression calculation. Rank stocks in the S&P 500 based on momentum. In his book, Clenow trades every Wednesday, but as he notes, which day is completely arbitrary. The momentum strategy defined in Clenow’s books trades based upon the following rules: A typical momentum strategy will buy stocks that have been showing an upward trend in hopes that the trend will continue. Momentum strategies are almost the opposite of mean-reversion strategies. Clenow’s book Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategy and backtest its performance using the survivorship bias-free dataset we created in my last post. In this post we will look at the momentum strategy from Andreas F.

Momentum drawdown