Rotationshandelssystem

When placed on the top of system formula it turns on rotational-trading (aka. fund-switching) mode of the backtester. Note: this function is now marked as obsolete. Use SetBacktestMode( backtestRotational ) in new formulas. IMPORTANT NOTE: Unless you specifically want to implement fund-switchingrotational trading system you should NOT use this mode. Rotational trading is popular method for trading mutual funds. It is also known as fund-switching or scoringranking. Its basic permise is to rotate symbols all the time so only top N issues ranked according to some user-definable score are traded. The number of positions open depend on Max. open positions setting and available funds position size. Once position is entered in remains in place until securitys rank drops below WorstRankHeld (settable via SetOption(WorstRankHeld, 5 ) ). Regular buysellshortcover signals are not used at all. The rotational mode uses only score variable (PositionScore) to rank and rotate securities. This idea has been implemented earlier in PortfolioTrader AFL formula written by Fred Tonetti with GUI written by Dale Wingo. To enter this mode you have to call EnableRotationalTrading() function at the very beginning of your formula. From then on using of buysellshortcover variables is not allowed. Only PositionScore variable will be used to rank securities and trade top N securities. A simple rotational trading formula (stocks with high RSI are best candidates for shorting while stocks with low RSI are best candidates for long positions): EnableRotationalTrading () SetOption ( WorstRankHeld. 5 ) PositionSize - 25 invest 25 of equity in single security PositionScore 50 - RSI () PositionScore has the same meaning as rScore in PT The score (PositionScore) for all securities is calculated first. Then all scores are sorted according to absolute value of PositionScore . Then top N are choosen to be traded. N depends on available funds and max. open positions setting. Backtester successively enters the trades starting from highest ranked security until the number of positions open reaches max. open positions or there are no more funds available. The score has the following meaning: higher positive score means better candidate for entering long trade lower negative score means better candidate for entering short trade the score of zero means no trade (exit the trade if there is already open position on given symbol) the score equal to scoreNoRotate constant means that already open trades should be kept and no new trades entered the score equal to scoreExitAll constant causes rotational mode backtester to exit all positions regardless of HoldMinBars. Note that this is global flag and it is enough to set it for just any single symbol to exit all currently open positions, no matter on which symbol you use scoreExitAll (it may be even on symbol that is not currently held). By setting PositionScore to scoreExitAll you exit all positions immediatelly regardless of HoldMinBars setting Exits are generated automatically when securitys rank drops below worst rank held . There is no real control over when exits happen except of setting low score to force exits. You can also set the score on any (at least one) security to value of scoreNoRotate to prevent rotation (so already open positions are kept). But this is global and does not give you individual control. Important: The rotational trading mode uses buy price and buy delay from the Settings Trade page as trade price and delay for both entries and exits (long and short) EnableRotationalTrading () SetOption ( WorstRankHeld. 5 ) PositionSize - 25 invest 25 of equity in single security PositionScore 50 - RSI () PositionScore has the same meaning as rScore in PTHistorical Data and Momentum Rotation Strategies We are going to take a slight detour with this post, and look at stockETFmutual fund rotation strategies. I actively trade rotation strategies in several accounts, and have been evolving my rotation strategies for about ten years. If youd like some information on how to build a rotation strategy, please take a look at the articles below: ETF Rotational System V1.0, Part 1 ETF Rotational System V1.0, Part 2 ETF Rotational System V1.0, Part 3 ETF Rotational System V1.0, Part 4 8211 Updated Roundup: FundzTrader Sector Rotation Strategy These articles are from the blogs of MarketSci and Woodshedder who have both written a number of posts (more than Ive listed above) on the topic of rotation strategies. Both seem to be semi-retired now and do not blog much. In my post we will not look at the strategies themselves, but rather the data that they use. A few years ago I began to realize that the signals I was receiving (and trading on) from my rotation strategies were occasionally inconsistent with the backtests of these same strategies. I didnt spend much time digging into the issue at the time, but it remained in the back of my mind until late December, 2013. For the trades that were generated from my systems for December 2013 (my rotation systems reassess monthly), I not only recorded the vehicles that were selected, but also their associated rotation rankscore. I have been tracking this information in spreadsheets since that time. a period which currently includes nine rotation cycles (9 months). In the middle of last month, August 2014, I decided to backtest my live rotation strategies across the same period that I had actively traded with these same rotation strategies. I was not surprised to find that a number of the trades in the backtests did not match the trades I had actually executed and recorded in my spreadsheets. I use AmiBroker and Yahoo end-of-data data ( Yahoo Data Info 1 . Yahoo Data Info 2 ) for my rotation strategies. I knew that Amibroker was configured by default to use the adjusted close rather than the actual close in its database, but I didnt think too much about this detail. I had been consciously using this adjusted close rather than the actual close for nearly ten years, but had not truly considered the impact of using adjusted close data with rotationranking strategies. The line in the AmiBroker aqh. format file that you should be aware of is highlighted below: If you would like to use the actual close rather than the adjusted close in Ambroker, replace the highlighted line above, with the line below and re-download all of your historical data from Yahoo. As a reminder, the adjusted close time series is a modified version of the actual close time series that includes gains from dividends and capital gains. This means that a buy price shown in a backtest will not be the actual buy price that you could have received trading on that day (for any stock or ETF that at some point later in time issued a dividend or capital gain). It is very important to think about this point and the impact it can have on your backtest versus live results This issue has a big impact on trade entry and exit signals with rotationranking systems. a system where a group of stocksETFsmutual funds are being compared to each other based on openhighlowclose (OHLC) price data. As an example, take a look at the historical data for the iShares Core US Aggregate Bond (AGG) . An excerpt of this historical data is shown in the image below. If your rotation system happened to be using adjusted close prices and had AGG in its basket of rotation vehicles, AGGs score for August 29 would have been different on August 29 when you traded it, than when you run your backtest for that date on say September 2 (after the dividend issuance). You will notice that the August 29 close is 109.98, but the adjusted close is 109.79. and this issue compounds with every dividend and capital gain that is issued. every past adjusted close is modified when a new dividend is issued. Take a look at the difference between the adjusted close and actual close just two years ago: The close on August 29, 2012 is 111.95, while the adjusted close on that date is 106.33. As new dividends are issued in the future, the 106.33 adjusted close price will get increasingly smaller, which will have an impact on the rotation rankingscore for AGG in all backtests. This same issue occurs with any vehicle that issues dividends and capital gains. If we use actual closing prices for our backtests, we will generate signals based on prices that actually occurred in the past. The trade off is that we will not see the positive impact of dividends and capital gains in the returns of our backtests. As an illustration, I can show the results of several rotation strategy variations run against the following basket of ETFs: AGG - iShares Barclays Aggregate Bond Fund DBC - PowerShares DB Com Indx Trckng Fund EEM - iShares MSCI Emerging Markets Indx EFA - iShares MSCI EAFE Index Fund GLD - SPDR Gold Trust IYR - iShares Dow Jones US Real Estate JNK - SPDR Barclays Capital High Yield Bond PPH - Market Vectors Pharmaceutical SPY - SPDR SampP 500 Trust TIP - iShares Barclays TIPS Bond Fund In the image below, you can see the equity curves for several different rotation strategies run against the 10 ETFs in the list above, but using the adjusted close time series. (click on the image to see a larger version).In the top pane, the green, purple, and red lines are the equity curves for three different rotation strategies run against the 10 vehicles in the list above. The other three curves are the buy and hold curves for SPY, IWM, and QQQ. The lower pane displays SPY (orange), and the same green, purple, and red equity curves from the upper pane. In addition, the lower pane contains seven equity curves for other rotation strategy variations on the same list of 10 ETFs. The y-axis is percent return, while the text for each strategy lists the cumulative dollar return for that strategy (the initial capital for each strategy was 100,000). Now lets look at the results for the exact same strategies run against the same 10 vehicles, but using the actual closing price time series data. We expect the returns to be lower, since dividends and capital gains are not reflected in the time series. The difference in equity curves is large, as expected. But how do the entry and exit signals compare between the adjusted close data and the actual close data. In the table below, is the comparison of entry and exit dates and vehicles for the strategy with the green equity curve in the above two charts. Recall that the equity curves in the two charts above was generated by the same rotation strategy run against the same 10 ETFs in the list above. the only difference between the equity curves is the data. adjusted close data versus actual close data. These are not vastly different selections between the adjusted close time series and the actual close time series with this rotation strategy and the 10 ETFs in the list above. I have noticed significantly greater differences with different baskets of ETFs and mutual funds. So whats the point Ideally, for rotationranking strategies we should generate our entry and exit signals based on the actual close time series, but calculate our returns on those trades using the adjusted close time series. If we can only use one time series, then we should consider using the actual close time series rather than the adjusted close time series. If we can live with equity curves that show smaller returns, using actual close time series data will result in generated entryexit signals in our backtests that match our actual entryexit signals that we received in real trading. And one last point. be sure to check the data that paid and free rotation strategy services use. its most likely adjusted close data. which means the signals they show in their backtests may not match the signals that they actually sent youSector Momentum - Rotational System Rotational trading systems in equity sectorsindustries are nearly as old as equity markets alone. Traders and investors have noticed that stocks from different sectors have different sensitivity to business cycle and have always tried to exploit this relationship. There exist several different approaches to sector rotation, and a rotation based on momentum is one of the most successful. The investment universe in our example contains 10 industry sectors, and the investor repeatedly picks equity sectors with highest momentum (past performance) into his portfolio. The goal of this strategy is to outperform simple buy and hold of equity index. There is a long only (strategy version which is presented here) and a long-short version of this strategy (where investors holds best performing sectors and shorts overall market or worst performing sectors). Fundamental reason Equity sectors have different sensibility to the business cycle therefore it is possible to rotate between them and hold only the sectors with the highest probability of gain and lowest probability of loss. The momentum anomaly is often explained by behavioral shortcomings, such as investor herding, investor over and underreaction and confirmation bias. Simple trading strategy Use 10 sector ETFs. Pick 3 ETFs with strongest 12 month momentum into your portfolio and weight them equally. Hold for 1 month and then rebalance. Source Paper Mebane Faber: Relative Strength Strategies for Investing papers. ssrnsol3papers. cfmabstractid1585517 Abstract: The purpose of this paper is to present simple quantitative methods that improve risk-adjusted returns for investing in US equity sectors and global asset class portfolios. A relative strength model is tested on the French-Fama US equity sector data back to the 1920s that results in increased absolute returns with equity-like risk. The relative strength portfolios outperform the buy and hold benchmark in approximately 70 of all years and returns are persistent across time. The addition of a trend-following parameter to dynamically hedge the portfolio decreases both volatility and drawdown. The relative strength model is then tested across a portfolio of global asset classes with supporting results. Other Papers Moskowitz, Grinblatt: Do Industries Explain Momentum faculty. chicagobooth. edutobias. moskowitzresearchindustry. pdf Abstract: This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly. Specifically, momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum. By contrast, industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences. Chen, Jiang, Zhu: Do Style and Sector Indexes Carry Momentum apjfs. org2009cafm20090403Do20Style20and20Sector20Indexes. pdf Abstract: Existing literature documents that cross-sectional stock returns exhibit price and earnings momentum patterns. The implementation of such strategies, however, is costly due to the large number of stocks involved and some studies show that momentum profits do not survive transaction costs. In this paper, we examine whether style and sector indexes commonly used in financial industry also have momentum patterns. Our results show that both style and sector indexes exhibit price momentum, and sector indexes also exhibit earnings momentum. Mostly importantly, these momentum strategies are profitable even after adjusting for potential transaction costs. Moreover, we show that price momentum in style indexes is driven by individual stock return momentum, whereas price momentum in sector indexes is driven by earnings momentum. Finally, using style indexes as illustration we show that performance of style investment can be substantially enhanced by incorporating the momentum effect. Andreu, Swinkels, Tjong-A-Tjoe: Can exchange traded funds be used to exploit country and industry momentum efmaefm. org0EFMAMEETINGSEFMA20ANNUAL20MEETINGS2011-Bragapapers0166.pdf Abstract: There is overwhelming empirical evidence on the existence of country and industry momentum effects. This line of research suggests that investors who buy countries and industries with relatively high past returns and sell countries and industries with relatively low past returns will earn positive risk-adjusted returns. These studies focus on country and industry indexes that cannot be traded directly by investors. This warrants the question whether country and industry momentum effects can really be exploited by investors or are illusionary in nature. We analyze the profitability of country and industry momentum strategies using actual price data on Exchange Traded Funds. We find that, over the sample periods that these ETFs were traded, an investor would have been able to exploit country and industry momentum strategies with an excess return of about 5 per annum. The daily average bid-ask spreads on ETFs are substantially below the implied break-even transaction costs levels. Hence, we conclude that investors that are not willing or able to trade individual stocks are able to use ETFs to benefit from momentum effects in country and industry portfolios. Szakmary, Zhou: Industry momentum in an earlier time:Evidence from the Cowles data efmaefm. org0EFMAMEETINGSEFMA20ANNUAL20MEETINGS2014-RomepapersEFMA20140079fullpaper. pdf Abstract: Virtually all evidence on the efficacy of momentum strategies arises from the post-1962 era, and momentum returns across different markets and asset classes are highly positively correlated. We examine industry momentum in an earlier time, and find these strategies would have earned returns over the 1871-1925 and 1871-1938 periods that are moderately similar to those in the modern era. We also show that the market state dependence of industry momentum strategies is similar between the two eras. Overall, our findings confirm that both the profitability and state-dependence of momentum strategies are pervasive and unlikely to be due solely to data-mining. Du Plessis, Hallerbach: Volatility Weighting Applied to Momentum Strategies papers. ssrnsol3papers. cfmabstractid2599635 Abstract: We consider two forms of volatility weighting (own volatility and underlying volatility) applied to cross-sectional and time-series momentum strategies. We present some simple theoretical results for the Sharpe ratios of weighted strategies and show empirical results for momentum strategies applied to US industry portfolios. We find that both the timing effect and the stabilizing effect of volatility weighting are relevant. We also introduce a dispersion weighting scheme which treats cross-sectional dispersion as (partially) forecastable volatility. Although dispersion weighting improves the Sharpe ratio, it seems to be less effective than volatility weighting. Geczy, Samonov: 215 Years of Global Multi-Asset Momentum: 1800-2014 (Equities, Sectors, Currencies, Bonds, Commodities and Stocks) papers. ssrnsol3papers. cfmabstractid2607730 Abstract: Extending price return momentum tests to the longest available histories of global financial asset returns, including country-specific sectors and stocks, fixed income, currencies, and commodities, as well as U. S. stocks, we create a 215-year history of multi-asset momentum, and we confirm the significance of the momentum premium inside and across asset classes. Consistent with stock-level results, we document a large variation of momentum portfolio betas, conditional on the direction and duration of the return of the asset class in which the momentum portfolio is built. A significant recent rise in pair-wise momentum portfolio correlations suggests features of the data important for empiricists, theoreticians and practitioners alike. Huhn: Industry Momentum: The Role of Time-Varying Factor Exposures and Market Conditions papers. ssrnsol3papers. cfmabstractid2650378 Abstract: This paper focuses on momentum strategies based on recent and intermediate past returns of U. S. industry portfolios. Our empirical analysis shows that strategies based on intermediate past returns yield higher mean returns. Moreover, strategies involving both return specifications exhibit time-varying factor exposures, especially the Fama and French (2015) five-factor model. After risk-adjusting for these dynamic exposures, the profitability of industry momentum strategies diminishes and becomes insignificant for strategies based on recent past returns. However, most strategies built on intermediate past returns remain profitable and highly significant. Further analyses reveal that industry momentum strategies are disrupted by periods of strong negative risk-adjusted returns. These so-called momentum crashes seem to be driven by specific market conditions. We find that industry momentum strategies are related to market states and to the business cycle. However, there is no clear evidence that industry momentum can be linked to market volatility or sentiment. Heidari: Over or Under Momentum, Idiosyncratic Volatility and Overreaction papers. ssrnsol3papers. cfmabstractid2687480 Abstract: Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question. Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover as another measure of overreaction, I present evidence that supports the investor overreaction explanation as the source of momentum effects. Furthermore, I show that when investor overreaction is low, momentum effects are more due to industries (industry momentum) rather than stocks. Related by markets: