2026-01-02 23:20:38

It's been a while since I produced new content, I've been refining my work in the quantitative trading pit. My recent discovery is: market trends are basically impossible to predict accurately, and success relies on following the market rhythm.

Some time ago, I integrated Chan's theory into my quantitative system. The principle looks good on paper, but when actually running it, issues arose. Using the central line to determine trading direction is a sound idea, but the problem is—over time, the amount of data accumulated for calculations increases, and the newly generated K-lines constantly alter the previously formed central structures. This creates a dynamic drift situation, especially when the market is changing rapidly, which significantly reduces the model's stability.

I'm stuck here now. Has anyone in the crypto space built a similar quantitative system, or used Chan's theory to optimize dynamic central structures? I especially want to hear how to handle this sequential update problem. Currently, I mainly focus on BTC, BNB, SOL, and other assets with good liquidity.

BTC1.73%

BNB1.7%

SOL4.21%

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Ser_This_Is_A_Casino

· 5h ago

Center drift is indeed a headache. I've tried it too, and later simply switched to a sliding window with a fixed period. --- Following the market rhythm really hits the mark. Predicting market trends is truly a pseudo-need. --- The combination of Chan Theory plus quantitative methods sounds good, but it's too difficult to implement. Data updates every second, and the entire logic collapses. --- Liquidity on the BTC side is good, but the volatility is fierce. Center drift in a ranging market is simply a nightmare. --- I feel the problem isn't with Chan Theory itself, but that you need to add a memory decay to the model so that outdated data doesn't influence current judgments. --- For sequential updates, maybe try layered centers—use long-term trend judgment with short-term trading. --- This pit is quite deep. I've now switched to event-driven instead of following the center.

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zkProofInThePudding

· 5h ago

Chán theory getting trapped is a nightmare, the central pivot keeps moving, who can withstand that? --- The longer the time, the more data accumulates, and the more problems arise. This logic itself is flawed. --- Predict the market? Bro, you're still too young. Following the trend is the way to go. --- Have you tried the freeze window on BTC? Feel like it might help alleviate drift? --- Getting stuck means you need to change your approach. Don't stubbornly cling to the central pivot. --- That's why I gave up on Chán theory. It's too complicated and actually causes more losses.

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DaisyUnicorn

· 5h ago

Haha, the central pivot in real trading is just a mischievous flower. The more you try to catch it, the more slippery it becomes. I understand that. The essence of the Chan Theory quantitative system is to use a static model to chase the dynamic market, which inevitably leads to failures. The time window is set too rigidly, or maybe try using time decay? Gradually reduce the weight of old data, and when new K-lines come in, rebalance. It sounds complicated, but it can indeed help mitigate drift. The slow fluctuations of the BTC market are manageable, but for SOL, which has monster-sized jumps on the minute chart, the pivot points are shattered before they even form. I suggest starting with the 4H chart to test the waters.

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GmGnSleeper

· 5h ago

The Chen Theory's approach to quantification can indeed be prone to failure, and I've also fallen into the trap of central pivot drift. Predicting the market is essentially self-deception; following the trend is the right way. Limit the time window? Don't use the entire historical data, only keep the recent N candlesticks for central pivot judgment and give it a try.

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Tokenomics911

· 5h ago

I just said that using Chán Theory to fit into quantitative trading is a trap. I've also encountered the issue of central pivot points drifting dynamically. --- Honestly, even if BTC works out, other coins might not, because liquidity differences are too significant. --- Try adding a fixed window? Don't let data pile up infinitely. --- That's why purely technical analysis needs risk control. No matter how sophisticated the model, it can't withstand black swan events. --- Chán Theory itself is a post-hoc strategy; applying it quantitatively is even more absurd haha. --- Use a Kalman filter for dynamic central pivot points. Someone discussed this on Telegram a few years ago. --- Getting stuck means you're on the right track. Keep focusing on this issue, and you'll definitely get it right. --- It sounds like parameter optimization. Try adaptive cycles? --- The market is so competitive now; relying on predictions isn't as good as combining risk control with position management. --- BTC is manageable, but dealing with the volatility of coins like SOL using Chán Theory central pivot points is really a pain.

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ChainDetective

· 5h ago

Getting stuck on the central drift in Chan Theory, honestly, it's quite frustrating. It feels like you've gotten to the core of the problem. Isn't this the most difficult part of quantification? The theory is elegant, but reality hits back—familiar recipe. Have you tried fixed window backtesting or not? You might have to sacrifice some sensitivity for stability.

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