Our backtesting engine found this bitcoin:native rule. It buys when the 30-day RSI of entities in profit crosses above its threshold. Since July 2023, its worst drawdown was 20%, against 53% for holding. Discover your own strategy 👇

The 4 BACKTESTING TECHNIQUES behind WINNING Strategies: i've spent the last 2 years running backtests on everything from mean reversion setups to volatility arbitrage to prediction market signals some strategies survived and tbh most of them died and the difference was never the strategy itself, it was how i tested it a backtest is not proof your strategy works, it's a stress test to see how easily it breaks here are the 4 techniques i've actually run, what worked, what broke AND what i still use --------------- technique 1: standard in-sample / out-of-sample split verdict: broken by default, NEVER TRUST THIS the setup is easy, take 5 years of data and train on the first 4, test on the last 1 the problem is subtle - every time you tweak the strategy and re-run, you're peeking at the test data and after 30 iterations your "out-of-sample" is FULLY contaminated the first strategy i ever backtested was a simple pairs trade between two energy stocks that showed a Sharpe of 2.1 on the standard split, so i deployed $2,000 of my own money and lost 40% of it in 3 months going back later i realized i'd re-run that backtest 47 times during tuning, the test data was never really untouched use this only for a quick first look, NEVER as the final validation --------------- technique 2: walk-forward validation verdict: the real workhorse, this is what i actually use instead of splitting once, you slide a window through the data train on 2018-2020, test on 2021 train on 2019-2021, test on 2022 keep sliding each test window is data the model has never seen and you get 5 or 6 test periods instead of JUST ONE what this catches: > strategies that only worked in one regime (the pattern shows up immediately) > parameters that shift wildly when retuned (unstable strategy, red flag) > strategies that survive across every window (this is real edge) at our fund we killed a stat arb strategy that showed Sharpe 2.4 on a standard split, but walk-forward revealed it worked beautifully in 2019-2020 and completely died in 2021-2022, the regime had shifted underneath us and it saved us months of losses but this is slower and more painful than a standard split and it's also the reason institutional backtests match live P&L :) --------------- technique 3: purged k-fold cross-validation verdict: fixes a hidden bug in walk-forward financial data has memory, today's price is not independent of yesterday's when your training window ends on december 31 and your test window starts january 1, information leaks across that boundary and your Sharpe looks better than it should purged k-fold fixes this, Marcos Lopez de Prado covers it in Advances in Financial Machine Learning the idea is simple: > split data into folds like standard cross-validation > when a fold is used for testing, remove the adjacent observations that overlap in time > this eliminates the leakage a QUANT friend of mine who runs an ML-based factor model showed me his numbers before and after adding purging, Sharpe dropped from 1.9 to 1.4 on the same strategy with the same data and the extra 0.5 was pure leakage he didn't know he had use this when you're training ML models on financial data, the leakage in tree-based models is brutal without it --------------- technique 4: monte carlo trade shuffling verdict: the reality check that saves capital EVERY SINGLE TIME your backtest shows one sequence of trades, Monte Carlo randomizes the order and runs it thousands of times why this matters: > your backtest might have gotten lucky with sequencing, what if the drawdown happened in month 2 instead of month 10 > the max drawdown you observed is one path, Monte Carlo shows the full range > the 5th percentile drawdown is often 2 to 3 times worse than what you saw few months ago (during the hype of 15-min BTC markets) i built a systematic prediction market strategy that showed 12% max drawdown across 18 months of backtest, but before deploying i ran Monte Carlo with 10,000 shuffled sequences and the 5th percentile scenario showed a 34% drawdown ofc i didn't deploy at full size, i sized it at 25% of what i originally planned and three months in the strategy hit a 22% drawdown, but the smaller size meant i could hold through it and the strategy recovered to finish the year up 31% Monte Carlo is why i stayed in that trade instead of blowing up --------------- what i actually use in production NOW: > walk-forward validation as the primary test > purged k-fold when the strategy uses ML models > Monte Carlo shuffling on strategies that survive both, before any real capital > standard in-sample/out-of-sample only for the very first pass (rarely tho) if your backtest is designed to make you feel good, it's designed to LOSE you money for real.

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Introducing Arkis Alpha: trade discovery and backtesting with Arkis' credit behind it. Most funding spreads look too small to trade. With Arkis portfolio margin behind them, the same spread can yield 5x. Alpha finds the opportunities that give you the return you want, shows the full economics after borrow costs, and backtests them. Live for clients today.

From a single prompt to a decade of backtesting. Sidekick tested dozens of $NVDA trading strategies. Uncovered one with an 848.6% historical return across 51 trades. ~1/3rd the drawdown of buy and hold And a reward to risk ratio of 7.08... No coding required.

Pi Network vs Major Memecoins We used @ElanInsights backtesting tool to see how the $PI token performed relative to the industry's top-100 memecoins. Try now: The memecoin index outperformed $PI for nearly the entirety of the past year... But that has changed. Since approximately mid-September, Pi Network has been up relative to the index. Is $PI a good one to hold long term...?

citadel's approach to researching a signal starts with a question almost nobody asks before backtesting. max bane, quantitative research lead there: "say we wanted to research some kind of momentum signal. why should something like momentum exist at all? why should it even arise? aren't markets supposed to be efficient?" "if we're going to hypothesise momentum, we should have in mind some kind of causal mechanism for why it should happen." two he offers: information propagates at finite speed. not everybody sees the news at the same time, so they act in sequence, and that creates autocorrelation in the price changes. or: someone with a large position splits it into smaller trades to control their transaction costs, and each one pushes price a little. here's why it matter: "the reason to go through this exercise of thinking through what the causal mechanisms are is that it gives you something to latch on to in terms of how you would test your hypothesis." if it's news propagation, go get data on how many people follow each stock. the theory predicts momentum is stronger where fewer people are watching. if it's institutional order splitting, quantify institutional versus retail interest. the theory predicts stronger trends in one than the other. "what is my theory, what predictions does it make, and how can i try to disprove the theory?"