8 Quantitative Trading Strategies (Backtests and Trading Rules) youtu.be/rlSkRMmycWo #Quantitative #tradingstrategies

MACD and RSI Strategy – 81.41% Win Rate 📈 The MACD and RSI strategy combines two momentum indicators to help identify potential trading opportunities. By adding a third indicator as a mean reversion filter, we can build a rules-based strategy and test how it performs. We backtest the following trading approach: ➤ We combine MACD and RSI with a third indicator as a mean reversion filter. ➤ When all three conditions are met, we buy. ➤ We exit when the mean reversion filter reverses, triggering the opposite of the entry condition. The rules are simple, but the results look promising. The strategy works best on stocks within a specific sector. Below is the equity curve for the strategy applied to an ETF. The backtest produced 169 trades, with an average gain of 0.62% per trade. Most years were profitable, while losing years showed relatively small losses. The worst year was 2008, with a loss of just 3.6%, compared with a 55% decline in the S&P 500. Can the strategy be improved or tested with different rules? If you have any suggestions, please comment 👇 #TradingStrategies

2 VIX Trading Strategies Today, we bring you 2 VIX trading strategies. The VIX is often called the market’s “fear gauge,” but can it also help you make better trading decisions? For a systematic trader, the key is turning changes in the VIX into specific, testable trading rules. In this article, we examine two VIX trading strategies and their historical backtest results. Both use volatility as a signal, but they take different approaches to identifying trading opportunities. We explain the entry and exit rules for each strategy, show the equity curves, and examine the returns and drawdowns. The trading rules are at the bottom of the article. VIX strategy 1 This is a breakout strategy. Strategy type: Volatility strategy. Market: US stocks (SPY). Performance No. of trades: 252 Average gain per trade: 0.75% Win ratio: 77% Profit factor: 2.5 Annual returns (CAGR): 5.5% Exposure/time in the market: 10% Risk-adjusted return: 52% (CAGR divided by time spent in the market (0.1)) Max drawdown: 23% Strategy 2 This is more like a VIX mean reversion. Strategy type: Volatility strategy. Market: US stocks (SPY). Performance No. of trades: 328 Average gain per trade: 0.5% Win ratio: 75% Profit factor: 2 Annual returns (CAGR): 5.2% Exposure/time in the market: 13% Risk-adjusted return: 38% (CAGR divided by time spent in the market (0.13)) Max drawdown: 23% Want More Backtested Trading Strategies? We publish rule-based trading research, systematic market observations, and new trading strategies for traders who prefer evidence over opinions. Join our Skool community, Rule-Based Trading: skool.com/rulebasedtrading/a… We add new strategy research daily and discuss how systematic traders can turn market ideas into rules that can actually be tested.

200 Day Moving Average Trading Strategy – (backtest and indicator) The main advantages of the 200-day moving average are simplicity, riding the trend, and playing defense. However, without a recession and falling prices, you are unlikely to beat buy-and-hold because of the many whipsaws. As with most things in life, the 200-day moving average comes with both pros and cons. The 200-day moving average strategy is no silver bullet. We backtest the following trading rules: * Buy when the close of the S&P 500 crosses above the 200-day average, * Sell when it closes below the average. This is a crossover system. It can't get any simpler than that! Here is the return (log chart shown below) of investing 100 000 in 1960 and reinvesting and compounding until today. Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #tradingstrategies

Monthly Momentum Strategy (ETF Sector Rotation Strategy In EEM, SPY, And TLT) In this short post, we give an example of a very easy and simple ETF rotation strategy among SPY (S&P 500), TLT (Treasury bonds), and EEM (MSCI Emerging Markets) that has worked pretty well over the last two decades. It has beaten “buy and hold” with lower drawdowns. That was until 2022, which was a very bad year for the strategy. We backtest the following trading rules: * It's based on monthly quotes in the ETFs SPY, EEM, and TLT. * Every month rank them based on last month's performance and go long the best performing ETF. * Hold for one month and repeat (or continue being long the same instrument). Without slippage and taxes the equity curves look like as shown below. Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #tradingstrategies

Larry Connors’ %b Strategy (Bollinger Bands) | Trading Strategies Explained In Chapter 5 of Larry Connors’ 2009 book High Probability Trading, he presents a trading strategy based on Bollinger Bands %b. Since several years have passed, it’s interesting to see how the strategy performs today. Connors tested 20 ETFs from their inception through the end of 2008, while we test them from inception to today. We backtest the following trading rules: ➤ The close must be above the 200-day moving average. ➤ The %b must be below 0.2 for three consecutive days. ➤ If both conditions are met, buy at the close. ➤ Exit when %b closes above 0.8. We couldn't replicate Connors’ exact results because we don't have his original parameters. However, the results look very good for QQQ and SPY, although there are very few trades. Below is the equity curve for QQQ (shown in the image). Can the strategy be improved or tested with different rules? If you have any suggestions, please comment 👇 #tradingstrategies

SPY Trading Strategy Using RSI In this post, we test an RSI-based trading strategy on SPY. The results support our hypothesis: RSI works well on SPY. Even better, we improve the strategy by adding one extra rule. We backtest the following trading rules: ➤ If RSI(2) is below 15, enter at the close. ➤ Exit at the close when today's close is higher than yesterday's high. Below is the accumulated profit curve for SPY, which tracks the S&P 500, from 1993 to today (shown in the image). Can the strategy be improved or tested with different rules? If you have any suggestions, please comment 👇 #TradingStrategies

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A 3-Day Gold Strategy With Less Than 10% Max Drawdown Today, we show you a 3-day gold strategy with a drawdown less than 10%. The trading rules are extremely simple: One rule to buy and one rule to sell. A 3-day gold strategy with less than 10% maximum drawdown sounds interesting, and the low drawdown comes from the strategy spending very little time invested. In this article, we examine a short-term strategy for the gold ETF GLD. Each trade lasts three trading days, and the backtest spends just 7.36% of its time invested. The idea is to capture selected short-term moves in gold. Across 203 historical trades, the strategy produced an average gain of 0.35% per trade, a win rate of approximately 62%, and a profit factor of 1.97. The maximum drawdown was 9.85%. The annualized return was a modest 3.22%, but that figure deserves context: the strategy was out of the market more than 92% of the time. For us, this makes it interesting as a potential component of a portfolio of strategies. Below, we explain the trading rules and examine the results. All figures are historical backtest results before commissions and slippage (which should be close to zero). A 3-Day Gold Strategy We backtested following GLD from its inception until today- · No. of trades: 203 · Average gain per trade: 0.35% · Average winning trade: 1.1% · Average losing trade: 0.9% · Average #daysper trade: 3 · Win ratio: 62% · Profit factor: 2 · Annual returns (CAGR): 3.2% · Exposure/time in the market: 7% · Risk-adjusted return: 43% (CAGR divided by time spent in the market (.07)) · Max drawdown: 10% Want More Backtested Trading Strategies? We publish rule-based trading research, systematic market observations, and new trading strategies for traders who prefer evidence over opinions. Join our Skool community, Rule-Based Trading: skool.com/rulebasedtrading/a… We add new strategy research daily and discuss how systematic traders can turn market ideas into rules that can actually be tested.

Trading SPY And S&P 500 Using VIX (Using Vix to trade SPY) Trading SPY And S&P 500 Using VIX (VIX Trading Strategies). The VIX is a popular measure of the implied volatility of S&P 500 index options. Put shortly, the VIX is a mean reversion indicator: when the risk premium increases (VIX is rising in value) it might be wise to buy stocks and sell when VIX drops in value. The VIX measures how much investors expect the stock market to swing up or down. When it goes up, it might be a good time to buy stocks, and when it goes down, it could be a good time to sell. This article talks about different ways to trade based on the VIX’s movements. In this post, we present backtested VIX trading strategies. We backtest the following trading rules: We're using a 10-day moving average for the BB. I like to use short time frames since they are more responsive. First, let's try with a standard deviation of 3 for the upper band. When testing, there is no fills using 3 STD. Here are the results using a standard deviation of 3 (Shown in the image below) Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #tradingstrategies

Triple RSI Trading Strategy: Boost Your Win Rate to 90% Today, we reveal a fresh perspective on RSI with the Triple RSI Trading Strategy. This innovative approach harnesses the power of RSI in a new and modified form, boasting an impressive 90% win rate. With just 83 trades since 1993, it may seem conservative, but each trade delivers a robust 1.4% gain on average. We backtest the following modified trading rules: * The 5-day RSI is below 30, and * The 5-day RSI reading is down for the third day in a row, and * The 5-day RSI reading was below 60 three trading days ago, and * The close is higher than the 200-day moving average, and * If 1-4 are true, then buy at the close. * Sell at the close when the 5-day RSI crosses above 50. We backtest SPY – the ETF that tracks S&P 500. When we put the trading rules above into Amibroker, we get the equity curve shown below. Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #tradingstrategies

3 Days Down Overnight Trading Strategy (S&P 500, Nasdaq, Rules, Performance, Video): Here is a simple mean reversion strategy for SPY that holds the S&P 500 overnight, from the close to the next day’s open. It’s one of the simplest approaches to testing overnight trading strategies. We backtest the following trading rules: ➤ SPY must be down three days in a row (from close to close). ➤ Enter at the close of the third down day. ➤ Exit at the next day’s open. Below is the equity curve for SPY, which tracks the S&P 500, from 1993 to today (backtested in AmiBroker). Can the strategy be improved or tested with different rules? If you have any suggestions, please comment 👇 #tradingstrategies

Best Day Trading Strategies: Most day traders use intraday data for backtesting and trading, such as 5-minute or hourly bars. But in this example, we’ll use daily bars instead. The reason is simplicity — daily bars are much easier to backtest and trade. In many cases, day trading with daily bars can be a smarter approach! We backtest the following trading rules: ➤ We short at the open on the last trading day of each month (not the calendar day, but the actual trading day). ➤ We cover at the close. We backtest the Russell 2000 futures (@RTY) from 2000 through the end of 2021, using 2022 as our out-of-sample test period. We use trading hours from 08:30 to 15:00 Chicago local time. Here’s how the strategy performed through 2022 (shown below). Can we improve the strategy or test some variations? If you have any suggestions, please share them in the comments! 👇

📈 5-Day RSI Strategy on Nasdaq: 18% CAGR with 34% Market Exposure Can a simple mean-reversion setup generate high historical returns while holding cash nearly two-thirds of the time? This strategy tests a 5-day Relative Strength Index (RSI) pullback model on liquid Nasdaq stocks. 📊 Historical Backtest Performance (Includes 0.03% per-trade slippage allowance; excludes 2000–2003 bear market) CAGR (Annualized Return): 18% Win Rate: 69% Profit Factor: 1.8 Average Winning Trade: +2.75% Average Losing Trade: -3.5% Total Completed Trades: 1,477 Maximum Drawdown: 28% Market Exposure: 34% 📈 Execution Rules & Constraints Universe: Liquid Nasdaq-listed stocks. Filter: Exclude low-priced stocks under $15. Capacity: Maximum of 3 open positions concurrently. Signal: Enter on short-term oversold conditions via 5-day RSI. 🔍 Key Lessons for Systematic Traders Capital Efficiency: Spending only 34% of the time in the market allows remaining capital to be deployed elsewhere or held in yield-bearing assets. Portfolio Diversification: Combine low-exposure mean-reversion systems with non-correlated trend strategies to smooth portfolio drawdowns. Forward Incubation: Track promising systems on live, out-of-sample data with frozen rules for 3 to 6 months prior to capital allocation. 💡 Key Takeaway High win rates and low market exposure make 5-day RSI mean-reversion an attractive portfolio component. However, historical backtests are baselines—always account for execution costs, drawdown limits, and forward validation. ABOUT QUANTIFIED STRATEGIES Run by former proprietary traders Håkan Samuelsson and Oddmund Groette, Quantified Strategies has published evidence-based research and backtested strategies since 2012. Our newsletter has 25,000+ subscribers, and our trading books have sold 30,000+ copies. 🚀 Want a more disciplined, evidence-driven trading approach? Join our Skool community for backtested strategies, quantitative research, and systematic risk management. 👉 Link in bio! #RSIStrategy #MeanReversion

Day Trading Price Action Strategy — Overview and Backtest: A day trading price action strategy refers to the pattern of price movement of an asset. Thus, a day trading price action trading strategy is using patterns of price movements to determine when to enter and exit a trade. We believe the trading rules are pretty straightforward: we want to enter a position after a series of lower lows and lower highs in a row. In this backtest, we use three lower lows and three lower highs. We use hourly bars. Let's list the trading rules: * We use hourly bars. * We enter at the close when the bar is the third lower low and third lower high in a row. * We enter a position only at 1030 local NY time. * We sell at the close of 1600 NY time. We backtest the WTI crude oil futures contract that is traded on NYMEX. The equity curve (2000-2021) looks like the image shown below. Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #tradingstrategies

MACD AND RSI STRATEGY - 81.41% WIN RATE 📈 The MACD and RSI strategy refers to a trading method that makes use of both indicators in analyzing and trading the markets. The combination of both momentum indicators may give more insight into what the market may do next and how it could move in the future. We set up a specific MACD and RSI strategy with trading rules so we can backtest it. * We incorporate both the MACD and RSI indicators, and we also use a third indicator as a mean reversion filter. *When all three conditions are met, we buy, and we sell when the mean reversion filter reverses (i.e. opposite from the buy criterium). This is it. It’s not very complicated, but it works well. The strategy works best on stocks in a specific sector. Below is what the strategy’s equity curve looks like on an ETF. There are 169 trades, and the average gain per trade is 0.62%. Most years show positive returns, while the negative ones show small losses. The worst year was 2008, with a modest 3.6% loss (compared to 55% for S&P 500). Can the strategy be improved or made different? If you have any suggestions, please comment 👇 #TradingStrategies