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8 Quantitative Trading Strategies
(Backtests and Trading Rules)
youtu.be/rlSkRMmycWo
#Quantitative #tradingstrategies
Why Confidence Should Come From Data, Not Motivation
Positive thinking cannot tell you how a strategy behaved during previous drawdowns.
Data can. Real trading confidence comes from understanding probabilities, losing streaks, and historical behavior.
Motivation may help you start, but statistics help you continue when things get difficult.
An interesting statistic about the US stock market:
The S&P 500 is still up approximately 12% in 2026.
Yet, among 5,454 US stocks tracked by Tradmap:
- Only 36% are above their 200-day moving averages.
- Just 25% are above their 50-day moving averages.
A remarkable sector divergence:
Consumer Staples (XLP): +5.02%
Technology (XLK): −7.70%
That's a 12.72 percentage-point difference in just five trading days.
Does an extreme defensive-vs-tech divergence like this predict a rebound in technology?
We backtested it. 👇
(A pretty good result for one day)
Does extreme Nasdaq underperformance predict a rebound?
We tested QQQ after it underperformed SPY by more than 3 percentage points over 5 trading days.
The idea is simple: when tech gets hit much harder than the broad market, does mean reversion follow?
We backtested it across multiple holding periods.
The results were interesting 👇
An interesting divergence in the US stock market last week:
Consumer Staples (XLP): +5.02%
Technology (XLK): −7.70%
That's a remarkable 12.72 percentage-point difference in just five trading days!
Meanwhile, 7 of the 11 major S&P 500 sectors actually gained, even though the S&P 500 declined.
This shows how misleading index performance can sometimes be.
A few heavily weighted sectors can drag down the entire index, even when most sectors are rising.
QuantifiedStrategies.com retweeted
Trend Following Strategy in S&P 500: (Meb Faber & Paul Tudor Jones):
Go long the S&P 500 when the price crosses the 200-day moving average, and sell when it crosses below the 200-day average. This is probably not the most sensational strategy, to say the least. However, Faber’s article is a great read because of his “simple” ideas, yet very powerful.
We backtest the following trading rules:
We buy when the close is above the 200-day moving average, and sell when it crosses below. The logarithmic equity curve above has a max drawdown of 28%, reached in 2002, and the CAGR is 6.45% while buy & hold is 7% (the test is not adjusted for dividends). S&P 500 trends little in the short term, but in the long term, it trends upward.
We go long when the close crosses its ten-month moving average and sells when the close is below the ten-month average. The equity curve looks like the image below since 1960.
Can the strategy be improved or made different? If you have any suggestions, please comment 👇
#tradingstrategies
You have to minimize your losses and try to preserve capital for those very few instances where you can make a lot in a very short period of time
- Jack D. Schwager
ALT Trading Quote - Jack D. Schwager
LARRY CONNORS’ DOUBLE SEVEN TRADING STRATEGY – BACKTEST AND EXPLAINED
In this post, we examine the performance of Larry Connors’ Double Seven trading strategy over the past 21 years across a wide range of liquid ETFs.
Does the Double Seven strategy still work? Yes, it still works, although Larry Connors has other strategies that have performed better.
We backtest the following trading rules:
The close must be above the 200-day moving average.
The close must be at a seven-day low.
If both conditions are met, go long at the close.
Sell when the close reaches a seven-day high, also at the close.
That’s all there is to it. No stop loss. It’s a simple strategy that can be tested in just two minutes.
We focus on the ETF with the longest history: SPY, which tracks the S&P 500.
SPY’s equity curve is shown in the image below.
Can this strategy be improved or modified? If you have any suggestions, please comment 👇
#TradingStrategies
ALT LARRY CONNORS’ DOUBLE SEVEN TRADING STRATEGY – BACKTEST AND EXPLAINED
Historical Data Is Only the Beginning
Historical testing is valuable, but it isn't the finish line.
The article explains that backtesting should be combined with real-time market analysis and ongoing learning so strategies remain relevant as markets evolve over time.
ALT Historical Data Is Only the Beginning
Trend Following Strategy in S&P 500: (Meb Faber & Paul Tudor Jones):
Go long the S&P 500 when the price crosses the 200-day moving average, and sell when it crosses below the 200-day average. This is probably not the most sensational strategy, to say the least. However, Faber’s article is a great read because of his “simple” ideas, yet very powerful.
We backtest the following trading rules:
We buy when the close is above the 200-day moving average, and sell when it crosses below. The logarithmic equity curve above has a max drawdown of 28%, reached in 2002, and the CAGR is 6.45% while buy & hold is 7% (the test is not adjusted for dividends). S&P 500 trends little in the short term, but in the long term, it trends upward.
We go long when the close crosses its ten-month moving average and sells when the close is below the ten-month average. The equity curve looks like the image below since 1960.
Can the strategy be improved or made different? If you have any suggestions, please comment 👇
#tradingstrategies