Swing Trading Systems
A quest for the ideal trading strategy
How do you find quality automated swing trading systems?
Backtesting tools such as SignalSolver can easily find profitable automated swing trading systems based on the historic price movements of stocks and ETFs. The real challenge is finding systems which continue to profit moving forward. On this site we publish interesting and often spectacular automated swing trading systems, then we track them to see if they remain profitable. Our aim is to improve the process of finding quality automated swing trading systems and to provide a discussion forum for everybody who shares this interest.
While the trading strategies published here would have given good returns in the past, there is no guarantee that they will perform well in the future.
The out of sample multi-algorithm approach was quite successful for AMZN (see results) so lets do it live and see how it ends up. On Dec 21st, I did the optimization using the EMA band and Figure of merit optimization I have been talking about in the last few posts. For the next 25 trading […]
This AAPL trading strategy would have given a return of $31,712,009 for every dollar invested in December 1980. That’s 123,000 times better than buy-hold, with nearly four times the annualized return (61.4% vs 16.7% for buy-hold) and roughly half the drawdown which amounts to over six times better reward/risk than buy-hold. AAPL $31,000,000 trading strategy: […]
In the interests of scientific method, I’d like to continue the multi-algorithm study by discussing the worst results, those for X (United States Steel Corp. ), NUGT(Direxion Daily Gold Miners Bull 3X ETF) and DUST(Direxion Daily Gold Miners Bear 3X ETF). There was plenty of potential for disaster in these stocks, very large swings indeed. […]
Oil had a rocky year, lets look at how our multi-algorithm approach worked on an oil related stock, UWTI (VelocityShares 3x Long Crude Oil ETN). In this post, I’m just going to summarize the results: Briefly, the method used is to run SignalSolver to find trading systems which worked for a 250 day period, then […]
Today, I’ll just throw out some more results from the multi-algorithmic testing I’ve been doing with SignalSolver. This time for Facebook (symbol FB). This is a stock which moved a lot in the time-frames used in the study. Could SignalSolver correctly predict and capitalize on these price movements? Let’s see… Optimizations for June 2015 through […]
Today I will be looking at some very interesting results of multi-algorithm testing on the stock AMZN (Amazon). The idea here is to use SignalSolver to find multiple trading systems which performed well in the past, then run those systems simultaneously on out-of-sample forward data to see what would have happened had you followed them […]
The portfolio and methodology for this multi algorithm study are described in my previous post. Here are links to the results spreadsheets: EMA/Figure of merit optimization periods: Sept 2014-15 Jan 2014-15 June 2015-16 PB/Return optimization period: Jun 2015-16 On these spreadsheets you can use the autofilter to include and exclude stocks of interest. For any […]
“Two heads are better than one”, the saying goes. But can averaging improve trading systems? Can a multi-algorithm technique improve profitability and lower risk? After compiling the “Summary of Strategy Performance”, I was very curious to quantify if and for how long strategies such as those published here were profitable for. To do that, I […]
This update covers 26 strategies published 2/10/15 through 9/18/15. For the analysis we measure the results of investing $10K in each strategy on the day of publication. 17 of the strategies made a loss over the entire period, however 5 of these were strategies that broke down after showing a profit. Overall Results vs Hold […]
This weekly trading strategy for IBB had 3 times better annualized returns than IBB and more than 10 times better return over the 10.1 years. For each 2.5 year period within the 10 years, the annualized return was between 30 and 50%.
This is a trading system for Keryx Biopharmaceuticals Inc. (KERX). It requires daily intervention, trading at the open of business. KERX Trading System: Trades Spreadsheet
A trading system which worked for FEYE (FireEye). This is based on daily data, so traded at most once per day. FEYE Trading System: Trades List As always, future performance is not guaranteed.
Not many trades, but performed well. TSLA Daily Trading System List of Trades Spreadsheet
This strategy has given about 1000x better return than buy-hold over the last 41.6 years.
Over 500 times better return than buy-holdIn the time frame May 16th 1997 to Aug 1st 2016, this strategy gave a return of 88.9% compounded which amounts to over $219,000 for every dollar invested, that’s 518 times better than the performance of buy-hold which only gave $423. This screenshot shows the strategy description and performance […]
This is a Berkshire Hathaway trading strategy which would have given almost ten times the return performance of buy/hold over the last 10 years with half the drawdown. The strategy is detailed in the table below, it was straightforward, with 123 trades over the 10 year backtest period. All trading was done at the weekly […]
Here is a Google Inc (GOOGL) trading strategy with once a week intervention which would have performed significantly better than buy-hold over the last 10 years. Annualized return was 31.5% vs. 16.5% (returning $149K for 10K outlay vs $36.7K, compounded), drawdown was 40.2% vs. 62.4%, so reward/risk was better. The buy and cover signal (see […]
Here is a Walt Disney Company (DIS) trading strategy with daily maintenance which had quite nice characteristics; 59% annualized return over the last 2 years. The performance was better than long buy-hold with lower drawdown and about three times the reward-risk. Signal reinforcement was good, and not many dual signal days. Below I show the […]
GLD is the much traded SPDR Gold Trust ETF. I find these two GLD trading strategies interesting because they gave reasonable results (32.6% and 48% annualized return) for each of the four 6 month periods of the analysis. The strategies require daily intervention. Strategy 1: BCS AHC This is a buy on fall, sell on […]
This TSLA trading strategy would have given a 1062% return over 2.1 years vs. a buy-hold return of 86% for the same period. The strategy is based on buying and selling when the stock price rises above specific thresholds. The buy side keyed off the day’s open price; the buy and cover signal appeared when […]
Today I present two TQQQ trading strategy backtest results with weekly setup with similar reward-risk but very different characteristics. TQQQ is the ProShares UltraPro QQQ, a triple leveraged ETF tracking the Nasdaq. The backtests were for the 288 weeks 2/11/10 through 8/14/15. The first trading strategy was found by optimizing the scanner for low drawdown, […]
Frequent reversals characterize this strategy for Omeros Corporation This OMER trading strategy is signal rich; there were 174 dual signal days out of the 528 days in the analysis. Added to that 151 buy signal only days and 39 sell signal only days and you get 364 signal days, of which 250 were actionable signals […]
This is an interesting AAPL trading strategy for the period 12/12/80 through 7/31/15, which would have generated $8,521,705,763 in profits from $10,000 initial investment. Buy and hold would have generated $2,744,894 in profit in the same period. The cover and buy signals were generated when the stock price dropped 37.77% below the 20 month exponential […]
This UWTI trading strategy would have returned $676,147 for $10K outlay over a 2 year period. It was a very straightforward strategy with simple maintainence, once a day you would have put in either market orders to cover and buy or stop orders to sell and short before the open. This is another result discovered […]
In contrast to yesterday’s TNA trading strategy optimized for low drawdown, this one is optimized for minimum quartus annual return, a new feature of SignalSolver. A quartus is one quarter of the data, 132 days in this instance, and the minimum return was 99% annualized for the most recent quartus Feb 3 to Aug 11th […]
Another low drawdown trading strategy-TNA Direxion Daily Small Cap Bull 3XIn the same vein as yesterday’s FAS analysis, here is a low drawdown trading strategy for TNA, with daily intervention. Prasad had asked me to search for low drawdown strategies for a few of the triple leveraged ETFs, this being one of them. As with […]
FAS (Direxion Daily Financial Bull 3X ETF) trading strategy with low drawdownThis is one of the triple leveraged funds which Prasad had asked me to come up with a low drawdown trading strategy for, I’ll be looking at the others soon. You can set up SignalSolver to optimize for low drawdown. The way you do […]
This trading strategy is for stock symbol DUST the Direxion Daily Gold Miners Bear 3X ETF. It is the complementary stock to NUGT which I analyzed last week. Its not hard to find strategies which would have exploited the intense volatility of this kind of security, if you have access to an optimizing backtester. The […]
This IBM trading strategy made 11x the total returns (4x the annualized return) of the underlying stock with half the drawdown. The backtest period was 10years. The strategy itself (see table below) was straightforward with both buy and sell signals triggered by falling prices. The buy signal keyed off the 52 week high while the […]
Original Post March 12th 2015Please note, this post was corrected 1-3-16 to account for a short-side error in the original calculations. Apologies for this. This NUGT trading strategy (Direxion Daily Gold Miners Bull 3X ETF) gave a theoretical $1.8 million profit for a $10K initial investment over 2.1 years. The trading strategy required once a […]
With $94 billion in profits (for $10K initial investment) this has one of the highest total returns I have seen for any algorithm. It is an interesting study, but I wouldn’t recommend it as a good system to trade moving forward. It is somewhat over-tuned, and the parameters are close to regions which would have […]
This is a GILD trading strategy with weekly intervention required. I found algorithms with better returns, but they were less consistent over time and had more parameter sensitivity. I chose this one because of many favorable characteristics:- Buy low, sell high type of algorithm, biased towards buying as befitting an upward trending stock like GILD. […]
This Palo Alto Networks PANW trading strategy would have given a 119% annualized return. Here, I chose to show only the long side of the algorithm because its more impressive than the short side (which only gave 27% annualized return). To appreciate the algorithm more, notice that the efficiency of the algorithm was close to 200%. […]
Algorithm for GOOGL based on monthly OHLC data and requiring monthly setup. Buy signals around 3 times more frequent than sell signals, some overlap (dual signal months shown as white lines). 80% long, 20% short. Algorithm showing flattening out of performance in recent months.GOOGL.M Performance table and strategy description. Showed about 3 times the reward-risk […]
Here is a strategy that worked on MSFT for the last 2 years. It gave returns around five times better than buy-hold, a total return of 360% vs. 70% for buy-hold. Drawdown was 10.6% vs. 17.9% for buy-hold. Backtest results are shown in the graphs below. MSFT daily trading strategy MSFT daily trading strategy, performance […]
Shows the results of backtesting a trading strategy for the stock SPY (SPDR S&P 500 ETF) over the 10 year period 1/18/05 to 2/23/15. This was a weekly strategy meaning that it needed setting up once a week, and it traded once a week or less. I chose this strategy because it gave the best […]
Micron Technologies monthly trading strategy, 46% Annualized Return, 25 yearsPlease note, this post was edited Jan 6th 2016 to correct an error in the short-side calculations. The original post showed an annualized return of 57%, which was erroneous. Below are shown the corrected results for this algorithm. Micron Technology (MU) trading strategy base on monthly […]
Update Jan 9th 2016 Update 10/19/2016
Update 10/19/2016 This strategy has lost around 22% since inception, the underlying stock has gained 48%.
K12 Inc (LRN) Update 12/27/2015 The above plots have been corrected for an error in the short side calculations. Below are updated equity curves and tables up until Dec 2015. The algorithm lost 13.6% over this time. The underlying stock dropped by 37.2%. Update 10/19/2016 Since first publication in Feb 2015, this strategy has outperformed […]
Add your own backtests and trading strategies
The above strategies were found by scanning through tens of thousands of trading strategies using a special backtester developed by the author. We invite fellow backtesters to add to the discussions by bringing their own backtest results to the table. Please feel free to comment on any of the results presented here and improve on them whenever possible. The more insight we can gain into the mysteries of stock price movements, the better!
Disclaimer: We make no claims that any trading strategy published here will work in the future. We will update from time to time to determine if the strategies had longevity beyond the time period of the original numerical analysis.