#backtesting

[isomorphismes]

The preparation of data for extensive back-testing requires a lot of patience. Just constructing a universe without survivorship bias is a fairly time-consuming process. 80% of my work is around getting the right data and getting it clean enough to get a useful result.

NuclearPhynance user dgn2

[ponotrading]

Testing Separates A Setup From A Story

Every trader has a setup that feels obvious after the candle closes.

The question is whether it survives testing:

Does it have clear entry conditions?

Can you define the failure point?

Does it work across multiple weeks, not one screenshot?

Does the result beat a random baseline?

Strategy is not confidence. Strategy is a rule set you can review when the trade is not going your way.

[quantifiedstrategies]

Seasonal Strategy for Bonds (Including Rule-Based Backtest)

Today, we look at a seasonal strategy for bonds, a part of the market many retail traders tend to overlook. That’s a pity, because bonds can be useful for strategy diversification.

I’ve always liked the quieter corners of the market. The places where the pace is slower, the stories are less crowded, and any edge, if there is one, tends to be small and subtle rather than dramatic.

Bonds fit that description for me, even though the bond market is larger than the stock market. They don’t attract the same attention, and maybe that is part of the appeal.

Over the years, I’ve noticed that certain seasonal patterns seem to appear again and again in bond markets. Not perfectly, and certainly not in a mechanical way, but often enough to make them worth studying.

This is not about predicting interest rates or making bold macro calls. It is more about observing whether flows, positioning, and calendar effects can tilt the odds slightly at certain times of the year, month, or even day of the week.

That is the idea behind this seasonal swing trade. Nothing fancy. Just a simple framework I’ve found useful to revisit from time to time.

I would not call it a complete strategy on its own. It is more of a lens. Sometimes it lines up with other signals I watch, and sometimes it does not. But when it does, it can add a little more structure to trades that might otherwise feel like guesses.

Seasonal Strategy for Bonds - Backtest

Let’s run a backtest of the strategy. The strategy trades both long and short in

$TLT

Seasonal Strategy for Bonds

No. of trades: 569

Average gain per trade: 0.43%

Win ratio: 60%

Profit factor: 1.6

Annual returns (CAGR): 10.1%

Exposure/time in the market: 62%

Risk-adjusted return: 16% (CAGR divided by time spent in the market (0.62))

Max drawdown: -24.

I believe these are pretty solid numbers for the seasonal swing trade in bonds, especially considering it also involves trading from the short side.

Trading Rules

Want the breakdown of the seasonal strategy for bonds?

[quantifiedstrategies]

Larry Connors %B Strategy – A Simple Mean Reversion Edge

Today, we’re looking at a classic idea from Larry Connors built around the %B indicator.

This is a mean reversion approach that focuses on short-term pullbacks within a broader trend. Instead of chasing momentum, it looks for moments when price stretches too far and is likely to snap back.

The %B indicator, derived from Bollinger Bands, helps quantify when the market is reaching those short-term extremes in a simple and objective way.

What makes this strategy interesting is its balance:

• It trades infrequently

• It aims for quick recoveries

• It focuses on small, repeatable edges rather than big moves

Our backtest on the S&P 500 shows relatively few trades over time, but with solid average gains and very low market exposure.

As with most mean reversion strategies, the profile is many small wins with the occasional larger loss, so execution and risk control matter.

If you’re interested, we walk through the logic, the indicator, and how this type of setup behaves over time.

Full breakdown in the video.

e occasional larger loss, so execution and risk control matter.

[quantifiedstrategies]

Bollinger Bands Breakout Strategy (Backtest)

Bollinger Bands Breakout Strategy for the S&P 500 is one of the most effective ways to capture volatility expansion and trade momentum in the stock market.

Most traders miss this.

When price pushes outside the Bollinger Bands, it is not random noise. It often signals a shift in volatility and a potential trend move just getting started. This is where opportunities begin.

Bollinger Bands are built on a 20 day moving average with bands set 2 standard deviations away. They expand during high volatility and contract during quiet markets. That dynamic behavior is what makes them powerful for breakout trading.

Here is how the strategy works in practice:

You buy when the S&P 500 closes above the upper band. You exit when price crosses below the lower band.

Simple rules. No guesswork.

We backtested this Bollinger Bands breakout strategy on the S&P 500 from 1960 to today.

Results: 58 trades 5.7 percent CAGR excluding dividends Invested only 64 percent of the time Maximum drawdown of 30 percent

This means you are not always in the market, yet still capturing meaningful upside during strong moves.

The key idea is not predicting the market. It is reacting to volatility expansion and letting momentum do the heavy lifting.

If you want a systematic way to trade the S&P 500 using data instead of emotions, this is a strategy worth understanding.

[quantifiedstrategies]

Options Expiration Week Effect (OPEX strategy) 📈

The market has a tendency to perform better during options expiration week.

The idea is straightforward: Buy the S&P 500 at the start of OPEX week → sell at the end of the week.

What’s behind it: As options expire, market makers rebalance hedges, often creating short-term upward pressure.

Stats from the data: • ~0.2–0.3% average weekly return • ~60% win rate • Invested only ~18% of the time

A simple pattern driven by market structure, not complexity.

[quantifiedstrategies]

A Simple Backtested Reversal Day Strategy

Markets often overreact in the short term. Sharp declines are frequently followed by quick rebounds, a behavior known as mean reversion.

A bullish reversal day typically occurs when the market trades weak during the session but closes strong. In this setup, the price makes a lower low during the day but finishes above the previous close, suggesting that selling pressure faded and buyers stepped in.

Below is a simple quantified version of a bullish reversal day strategy.

Trading rules

Enter a long position when all conditions are met:

Today’s low is lower than yesterday’s low

Today’s close is higher than yesterday’s close

The 5-day RSI is below 35

Exit the position after N days.

The idea is straightforward. The lower low shows intraday weakness, while the higher close indicates strong buying into the close. The RSI filter ensures the market is already in an oversold condition.

Interestingly, this setup does not work particularly well in stocks. In equities, it often performs better to buy weakness directly rather than waiting for a reversal close.

However, the strategy produces more promising results in gold.

Backtest Results

We tested the strategy on the gold ETF SPDR Gold Shares. The system enters on the close when the conditions are met and exits after a fixed number of trading days.

Across holding periods from 1 to 20 days, the strategy shows positive results. The best average gain per trade occurs when exiting after 20 days, with roughly 1.5% per trade. Profit factor remains above 1.8 across many exit periods.

The main drawback is that signals are rare. Since GLD’s inception, the strategy has produced only about 70 trades.

Final thoughts

Many robust trading strategies come from simple ideas such as oversold conditions and mean reversion. However, edges are often market-specific. A strategy that works in one asset class may perform poorly in another.

Because of that, the Key Reversal Day strategy or any strategy should always be tested on the specific market you plan to trade.

[definedge99]

Opstra Options Screener: Data-Backed Trading Ideas 📈📊

Stop second-guessing your entries. Raghunath and Abhijit Phatak introduce the new Opstra Options Screener, a revolutionary tool that delivers live, backtested trading ideas for all F&O stocks and indices. Unlike basic scanners, it provides historical win rates and risk metrics, allowing you to execute strategies that are proven by data rather than just signals.