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This is a fascinating experiment. Crypto markets are notoriously noisy but tracking metrics like MAPE and RMSE is the right way to see if your models actually have an edge over a simple 'Buy & Hold' strategy. A few technical questions/thoughts. Data Granularity. Are you using 1-hour or 1-day intervals for your forecasts? Sentiment Integration. Have you considered adding a sentiment analysis layer (like X/Twitter or news trends) to see how it affects the confidence intervals? Backtesting: It’s great that you’re including historical backtesting. Does the model perform better during high-volatility periods or sideways markets? I'll definitely check out https://www.google.com/search?q=c-pred.com. Projects like this that focus on data transparency rather than 'get rich quick' hype are what the community needs more of.
Post is by: Short-Cantaloupe-899 and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1ro0v3y/can_statistical_models_actually_predict_crypto/ I’ve been experimenting with a small project that tries to generate short-term crypto forecasts using several statistical models. The goal isn’t to claim “price prediction”, but to test whether combining multiple simple models can produce something slightly better than random direction guesses. Right now the system generates a **7-day forecast** and tracks how accurate those predictions actually are over time. Some things it currently includes: • multi-model forecasts (Drift / Oracle / Axis / Forge) • forecast ranges and confidence intervals • model performance tracking (MAPE, RMSE, directional accuracy) • a simulator comparing model-based trading vs buy & hold • backtesting using historical data • a simple monitor comparing live price vs expected close Example: today the system forecasts BTC around **\~$67.9k** with a predicted range roughly between **$64k and $71k** over the next few days. I’m mainly trying to answer a simple question: **Are short-term statistical forecasts in crypto actually useful, or are markets too noisy for this to work?** If anyone wants to explore the tool or share feedback on what looks wrong / misleading / useful, I’d really appreciate it. The project is here: [**c-pred.com**](http://c-pred.com) *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*
This is the next big mover, combining the two hottest trends PEPE + TRUMP = MAPE with huge names behind such as whalefud and superb community, dont fade on the next billions token
This is great. And thank you for the awesome lookahead question! Two things: What about DCAing into a strategy? That way you sort of get an interesting hybrid. Regarding lookahead, I'll provide a detailed response from the strategist of the ETH-BTC strategy. It is a bit long, but hopefully you appreciate the detail. The part dealing with lookahead is in the second paragraph, but I've included the longer response for full context. Let me know if you have other questions. I'd love to answer them. *The general background about our strategies is that we can see two major market cycles in crypto (this is of course subjective view because we did not consider the early days of crypto). One peaked in Jan 2018 and the other in Nov 2021. And during our 20+ years of being in the business, we’ve seen more market cycle in various TradFi markets as well. So, we took the lessons from previous cycles and implemented a general model and backtested how it works for a cycle starting from 2019 for our crypto strategies. We saw that it worked quite well. The only problem, it discards recent period which already has more data about various crypto assets. So, we implemented “another layer” that considers new data as well. The overall model has other “layers” that have to make decisions about portfolio asset allocation proportions etc. So the models become “slightly more complex”.* *Since we are dealing with market cycles, we have the problem that generally markets tend to behave differently in various market phases (e.g. google for: volatility asymmetry around the world). When using a traditional training/validation/testing process we are either throwing away data or getting unexpected results due to changes in market conditions. To (somewhat) overcome that, we used a cross validation type of approach; or iteratively train a new model. The problem is of course that the simplest type of cross validation may not work that well because we are dealing with time series data with autoregressive properties. And one can always say that we can introduce look-ahead bias with that. So we carefully considered all of that when implementing the models and did our best to avoid any biases or overfitting, and still make as much use of recent data as necessary. It will not be that difficult or complex in the end but those are just some factors that need to be considered. And it does not make implementing the models just as straight forward as choosing a training and a validation period, implementing a model, and we are good to go.* *Another factor to keep in mind is that we are not predicting the market with those strategies but reacting to market signals and assessing the probability whether it is worth keeping or dropping market exposure. Thus, any traditional data science/machine learning model evaluation metric such as RMSE or MAPE is not that relevant for the overall objective. Even with ensemble approach, we also executed human judgement during the development phase, what to include and what not to include in the final model. Some like that approach, some may dislike it, but it’s our philosophy that human judgement during model development phase can help to improve the results, but any human biases will be taken out during the strategy execution phase.*
You tricked me. LOL. I was actually expecting to see a long list of memecoins and how they made your financial life miserable. I guess you've made the right choice. I also don't have a big bag of tokens, I noticed buying into a lot of projects can eventually land you in a loss than a profit. This is why besides major projects BTC & ETH, I only hold projects like DIA, DAFI, & MAPE for the long term.
Well, anyone that's been in the market for so long will be used to this and know that there's no better time to buy than now. Buying all my gems at a discount homie - AXS, MANA, MAPE. But surprisingly, the Mecha Morphing MAPE is up 11% today despite BTC dipping -9%. This is impressive. I guess this is as a result of the team announcing token farming launching on babyswap.
MechaMorphing, a play to earn game launched mainnet last week. I received some of their MAPE coin for free in a contest, so I’m hoping it climbs with time.
Well OP. You are entitled to your opinion. I like the stepn thing, but still can't wrap my head around why you related it to gaming. I don't know if they ship anywhere in the world, I'll be glad to get one, use it on the threadmill. I'll run for 2hrs straight at that speed just watching CNN and all the troubles going on around the world. Now to my argument, P2E games have a degree of ponzinomics. Maybe I'm not getting something here. Using a project I follow, the Mecha Morphing game as an example. You buy it's NFTs to battle in-game, you have some of its governance tokens for the DAO, you play, you win MMC tokens, you convert them into MAPE (it's governance token). Ehm... where's the Ponzi in that please?
Red is opportunity. I'm taking the chance to accumulate more gems I wished I could buy at cheaper prices when the market was green. I am also bullish on BTC, ETH, DOT, SOL, OIN, OCEAN, SAND, CVX, MAPE, TEAM (IDO this April) and a few projects in the cosmos ecosystem. We are so early in this space and there are tons of opportunities waiting for us investors and traders. It really is a good day to be in crypto.