Succeed in Financial Markets with Predictive Intelligence on Equities, Crypto and Commodities | Bittensor SN50

Joined March 2025
Synth's most ambitious project into frontier markets is underway
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92 days on SN50. Up to $38,231 in mining rewards. To every miner raising the bar in Q3: thank you. Think you can do better in Q4? Come compete.
🤖 Made with AI
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New forecasting models keep arriving. How do Synth’s miners stay competitive? Our latest research adapts @GoogleResearch's TimesFM-3 to Synth’s crypto forecasting task, testing it against our established Chronos-2 system using historical market replays. Three things stood out: → Adaptation matters. Changes to inputs, training and the construction of possible price paths took TimesFM-3 from a median rank of 196th to 2nd during our May tuning tests. → A newer model doesn’t automatically win. Our adapted Chronos-2 system led on score in four of five later evaluation periods. TimesFM-3 led in September. → Efficiency counts too. The Chronos-2 system took roughly one-sixth of the time per forecast in our CPU tests. For miners, staying competitive means continually testing models, refining methods and understanding where each approach performs best. For Synth, this research helps identify what makes forecasts useful, and gives participants clearer directions to explore. Read the full research below. @rsen91
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Full paper: synthdata.co/research/how-de… Building a miner? Use Synth’s backtesting tools to test your forecasts against its scoring system and recorded competitors before registering: github.com/synthdataco/synth… The paper explores where adaptation helped, where performance varied, and the trade-offs between forecast quality and compute. Research note: TimesFM-3 carries a non-commercial licence. We evaluated it offline; no TimesFM-3 miner was deployed, and no model weights or derivatives were distributed.
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Full paper: synthdata.co/ Building a miner? Use Synth’s backtesting tools to test your forecasts against its scoring system and recorded competitors before registering: github.com/synthdataco/synth… The paper explores where adaptation helped, where performance varied, and the trade-offs between forecast quality and compute. Research note: TimesFM-3 carries a non-commercial licence. We evaluated it offline; no TimesFM-3 miner was deployed, and no model weights or derivatives were distributed.
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Synth Ultra expands beyond Bitcoin. ETH, SOL and XRP are now live, each with its own ranking and miner rewards starting today. The challenge: forecast price distributions 10 seconds ahead, targeting inference under 5ms. Different markets bring different dynamics - opening more scope for specialised forecasting models, tested continuously against live prices.
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Synth Ultra applications are open again. We’re opening one new miner slot each day for the next 20 days, welcoming more distinct forecasting models into our high-frequency competition. Applications are prioritised in arrival order. Apply once: your place in the queue carries forward until a slot becomes available. One operator, one slot. Each model must be distinct, and applicants need a registered UID on SN50. Full instructions: github.com/synthdataco/synth…
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Synthdata retweeted
Imagine being both @SynthdataCo
Small RL companies are the AI industry's equivalent of quant shops. Let me explain. Both are small, secretive, full of brilliant people, profitable, and always one step from their edge disappearing. The parallels: - Secretive. Nobody publishes their signals. Labs won't say what they're training next, and vendors can't say who they sell to. Epoch calls the market "largely behind closed doors." - Hunting alpha. For a quant, a trade stops working once everyone finds it. For these shops, a task stops paying once models can solve it (labs drop tasks at around 70% pass rate). Meanwhile, labs are building their own in-house. So they always have to find the next field. - Deep Industry Connections. You need to know the problem cold to get in. Founders have usually worked at, or sold to, the labs. You can't define "correct" for fund accounting without having done fund accounting. - They buy strange data. Quants buy satellite photos of parking lots. RL shops bid for enterprise data (eg $12.5M bid for Spirit Airlines historical data). - Brilliant people. Quants hire math olympiad winners. These shops hire ex-lab researchers and real practitioners. - Huge revenue per head. Jane Street made about $11M per employee in 2025. Proximal is about $5M. The tweet's company would be $20M. - One narrow problem that makes something more efficient. Quants make markets more efficient. These RL shops make models training more efficient. - High risk (for different reasons). A quant can lose millions in a day on a bad trade. An RL shop's risk is that one lab trains a model that makes their environments irrelevant, or brings the work in-house, and the revenue stops. - Overfitting/reward hacking. A quant's nightmare is a strategy that looks perfect on historical data and fails live. An RL shop's nightmare is an RL grader that scores perfectly and trains the model incorrectly. - Infrastructure arms race. Quants pay for microseconds of speed. RL shops race to spin up thousands of disposable computers per training run. Obviously there are as many differences between RL companies and quant shops, but right now the aura around them feels eerily similar. Curious to see how this market matures as robotics opens up a new surface, and whether the frontier labs end up absorbing them.
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They are not wrong
Today @SynthdataCo was described as the world's best eval for asset price forecasting
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1/ Introducing Volatility CRPS Updated As we continue our mission to build the world’s most accurate financial forecasts, we’re introducing a new scoring mechanism to our competition: Volatility CRPS is now live in the Crypto 1h competition, directly scoring how accurately each model forecasts the volatility implied by its simulated price paths. Read on to learn how this update directly improves Synth's trading performance
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5/ That matters directly for Synth’s trading. Better volatility forecasts can improve: • market-making spreads • position sizing • execution timing • options and volatility pricing • liquidation-risk estimates • hedging decisions The goal is to turn better forecasts into better trading decisions.
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6/ This also gives miners another way to outperform. Models that forecast both price and the shape of volatility more accurately can earn more reward and contribute more useful signals to Synth’s trading systems. The closer each simulated path is to a plausible future market state, the more valuable the competition becomes. Full post here: synthdata.co/research/introd…
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Excellent presentation by @const_reborn We are proud to be a leading subnet by revenue and excited to continue contributing to the growth of Bittensor's intelligence
Here is @const_reborn's full keynote at @ExploitSummit It is worth the 36 minutes, but I'll give you the cliff notes. > Affine 1, a model trained by anonymous miners entirely on Bittensor, matches the intelligence score of Anthropic's Opus 4.1. > 23 of 128 subnets now have paying customers > Gamma tokens are coming to turn subnet usage credits into something agents can buy from any chain. > "We're not afraid of hyper-intelligent bot swarms on the internet. We use them to optimize our technology."
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Ultra getting an upgrade soon
Milliseconds matter more than ever. Introducing Synth Ultra, our new ultra-low-latency modelling competition. Models compete to predict the distribution of asset prices 10 seconds into the future, with less than 5ms latency. Apply below. Launching August 20.
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Another killer use case for the Synth Ultra 10 second predictive price modelling competition
There is a new LP meta game on @RobinhoodCrypto of using @Uniswap uniV4 hooks as a substitute for mempool JIT (just in time) liquidity as this isn’t possible on arbitrum orbit with to fifo ordering So you use a v4 hook to mint a tight range from inside it and get infront of the swap. Sometimes with a 5% pool fee lol Especially useful for tokenized equities if you have access to a nasdaq feed or powerful predictive models like we do @SynthdataCo Gg
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👀👀
Longing SN50 Synth before everything settles with the new Root Reborn update. We're looking at one of the best deep value subnets on the Bittensor bittensor:native network. Down 75% over the past 300 days... What stands out the most to me is the ARR is approx $1-$2M reported by @WeAreSubConnect in their new report (Absolute must read if you're interested in Bittensor). This makes them one of the highest earning subnets within the network. Extremely undervalued in my opinion. A bounce towards its next resistance zone is about 100-135%. Give me a reason why we all shouldn't ape this...
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