Gate

roux

hedge strategy

Golden

directional agent

Elle

market-making agent, details to be revealed in official submission

Racing for

Bitget

ctrader xt

Market making both spot and perps

Racing for

Gate

I want to build an evidence-gated market-making agent for Gate’s BTC/USDT perpetual market using a Hummingbot V2 Controller with a constrained Condor monitoring layer. The controller will quote both sides continuously for volume, while adjusting spread, order size and inventory skew using realised volatility, order-book depth, recent fill quality and current exposure. What makes the strategy different is its focus on measured execution quality rather than backtest profit alone. After every fill, it will measure short-horizon markout, fees and adverse selection. It will quote more aggressively only when the observed net edge supports it, and switch between pre-tested tight, normal, defensive and paused modes when conditions deteriorate. Deterministic safeguards will cap leverage, inventory, drawdown and daily loss, and will stop trading on stale data or account-state mismatches. I have already built the research and paper-trading foundations for this approach in Hummingbot, including chronological walk-forward testing, data-quality checks, realistic fee and missed-fill modelling, immutable order/fill records and fail-closed execution. The goal is to generate competitive volume without treating volume that loses money as success.

Racing for

Gate

Jadonamite Kenechukwu

Creativity Peaked

A market-making agent on Hyperliquid perpetuals — with the edge in position sizing rather than quoting. Dynamic spread adjustment on volatility and inventory is table stakes; every serious entrant will have it. Most 48-hour races aren't lost on bad spreads, they're lost to inventory blowup. So my differentiator is a capital-pacing controller I've already built and tested, which ports TCP congestion control — Google's BBR — to capital deployment. Instead of trading until it hits a risk limit (the loss-based behaviour of 1980s TCP), it continuously models a ceiling from measured value-rate and result-latency, paces inventory below it, and probes upward only when the model says there's room. Underneath sits a hard floor — per-position, total inventory, rolling-24h — that holds regardless of what the model believes; a test fires 10,000 retries at it and proves it cannot overspend. The transplant has precedent: Netflix took BBR's insight out of the network and into RPC concurrency limits. This is the second hop — requests to capital. Volume comes from quoting both sides continuously; survival comes from the controller. In a race scored on volume and P&L over a fixed window, the agent still standing at hour 47 wins.

Racing for

Botcamp

Nivesh Gajengi

i want to make Trading Strategy bot that can be used to backtest trades and then create stratergies to use for other agents

Racing for

Meteora

Sergiu O

Solo builder, Chisinau. Market microstructure and liquidation data. 25 services in production.

quench is a market maker for Bitget USDT-M perpetuals, built as a Hummingbot V2 controller. It quotes both sides in units of realised volatility, and every exit scales with the quote that filled it, so a fill five volatility units away from mid targets its way back toward mid instead of a fixed take profit. That single detail is what decides whether a wide quote pays for itself. On top of the quoting sits a liquidation fuel map. A collector reads open interest changes off the perpetual tape and projects them into leverage-implied liquidation clusters above and below price, marking a cluster spent once the tape has traded through it. The agent will not sell into unspent short-liquidation fuel above it and will not buy into long-liquidation fuel below it. When the feed goes stale the layer switches itself off and the agent falls back to plain volatility-scaled quoting. It never acts on stale data. I ran it inside Hummingbot's own V2 backtesting engine over fourteen days of one-minute SOL data, with 34 offline tests, and I will tell you what the numbers said rather than what I wanted them to say. Quoting one volatility unit wide loses money at a 2 bp maker fee, because gross edge per round trip is 2.3 bp. Quoting five and ten wide earns 5.7 bp gross, clears the fee, and returns a t-statistic of 2.4 across 53 fills with both halves of the sample positive. The liquidation layer as first written cost 11 percent of net, so I cut the component responsible instead of keeping it for the story. Solo builder in Chisinau, running about 25 services in production on my own box with watchdogs. The agent will still be alive at hour 47.

Racing for

Bitget

Due Diligence by Top Traders

looking for new algos for my portfolio

Racing for

Bitget

kenneth umoekpe

Regime-Aware Solana Liquidity Agent Build a Condor agent that autonomously provides concentrated liquidity on Meteora or Orca while protecting its capital from volatile or unsafe pools. The agent would: Scan eligible Solana pools. Rank them using fees, liquidity, volume, volatility, and token-risk signals. Detect whether the market is trending, ranging, or becoming unstable. Open concentrated-liquidity positions only when expected fee income justifies the risk. Dynamically widen, narrow, or reposition its liquidity range. Exit when volatility, drawdown, pool quality, or impermanent-loss risk becomes excessive. Explain and log every decision. Enforce hard capital limits independently of the LLM.

jilt jeeltcraft

creative developer

the agent operates in the omnity ree network with its own LP and flash loan infrastructure, across evm, ICP and Bitcoin networks, atomically.

Racing for

Orca

Alan Coppola

Trading agent description: This agent implements an active, tight-range concentrated liquidity market making strategy on Orca Whirlpools (Solana), targeting the SOL/USDC pair. Rather than deploying a passive full-range LP position, the agent continuously monitors price and maintains an ultra-narrow tick range (±1–2% of spot) around the current market price to maximize capital efficiency and fee capture per dollar deployed. Strategy type: Active concentrated liquidity provision with automated range-rebalancing. When price exits the current tick range, the agent closes the position and immediately reopens a new tight range centered on the updated price — converting a normally passive AMM LP position into an actively managed market making strategy. The agent also applies a directional skew (asymmetric range placement) based on short-term momentum signals, biasing liquidity toward the side price is more likely to move, which increases fee capture on directional moves while still collecting fees if price stays range-bound. Markets/exchanges: Trades exclusively on Orca Whirlpools on Solana, accessed via Hummingbot Gateway's DEX connector. Primary pair is SOL/USDC, chosen for deep liquidity, high trading volume, and Orca's 0.30% base fee tier with adaptive fee scaling during volatility spikes. What makes this unique: Most LP bots optimize for long-term stability and minimize impermanent loss through wide ranges. This agent is optimized for short evaluation windows, deliberately trading capital efficiency and IL risk for maximum fee-per-dollar throughput — effectively acting as "leverage" on a spot AMM without any borrowing, since a ±1% range can achieve over 200x the capital efficiency of a full-range position. Backtested rebalancing thresholds and range parameters are validated offline using a zipline-reloaded–based fee/IL simulation before being deployed live via Hummingbot Gateway, bridging traditional quant backtesting infrastructure with on-chain execution. Prepared with help from Perplexity Deep Research

Racing for

Orca

Kristian Mikula

I'm building an automated liquidity-management agent for Meteora's DLMM pools on Solana, using a Hummingbot Controller connected through the Gateway connector. It opens a concentrated-liquidity position centered on the current price, monitors the pool continuously, and automatically closes and re-opens the position whenever price drifts outside the active range so capital keeps earning fees instead of sitting idle out of range. I'm starting with a simple, well-tested bin distribution and conservative rebalancing thresholds to limit churn and impermanent loss, with room to add volatility-based sizing as I iterate. This is my first Solana/DeFi build, so I'm prioritizing something simple and reliable over something exotic.

Racing for

Meteora

Nirmalandu Das

I want to build an AI crypto trading agent that thinks like a skilled discretionary trader. It will trade BTC, ETH, and highly liquid altcoins by analyzing market structure, liquidity, volume, momentum, volatility, and multi-timeframe trends. The agent will adapt to changing market conditions, avoid low-quality setups, and dynamically manage position size, stop-loss, and take-profit. Its key advantage is knowing when not to trade. Every trade will have a clear reason, risk level, and invalidation point. The goal is disciplined, explainable, risk-adjusted trading not endless signals or unrealistic win-rate promises.

Half Doctor

The Derive Volatility Spread Trader is an autonomous quantitative agent that systematically harvests crypto options volatility risk premia on Derive. By combining forecast RV vs. IV edge modelling with real-time Dealer GEX intelligence from Derivatives Monkey, it executes atomic 4-leg RFQ option packages and maintains strict delta neutrality via zero-fee perpetual rebalancing.

Racing for

Derive

roux

hedge strategy

Golden

directional agent

Elle

market-making agent, details to be revealed in official submission

Racing for

Bitget

ctrader xt

Market making both spot and perps

Racing for

Gate

I want to build an evidence-gated market-making agent for Gate’s BTC/USDT perpetual market using a Hummingbot V2 Controller with a constrained Condor monitoring layer. The controller will quote both sides continuously for volume, while adjusting spread, order size and inventory skew using realised volatility, order-book depth, recent fill quality and current exposure. What makes the strategy different is its focus on measured execution quality rather than backtest profit alone. After every fill, it will measure short-horizon markout, fees and adverse selection. It will quote more aggressively only when the observed net edge supports it, and switch between pre-tested tight, normal, defensive and paused modes when conditions deteriorate. Deterministic safeguards will cap leverage, inventory, drawdown and daily loss, and will stop trading on stale data or account-state mismatches. I have already built the research and paper-trading foundations for this approach in Hummingbot, including chronological walk-forward testing, data-quality checks, realistic fee and missed-fill modelling, immutable order/fill records and fail-closed execution. The goal is to generate competitive volume without treating volume that loses money as success.

Racing for

Gate

Jadonamite Kenechukwu

Creativity Peaked

A market-making agent on Hyperliquid perpetuals — with the edge in position sizing rather than quoting. Dynamic spread adjustment on volatility and inventory is table stakes; every serious entrant will have it. Most 48-hour races aren't lost on bad spreads, they're lost to inventory blowup. So my differentiator is a capital-pacing controller I've already built and tested, which ports TCP congestion control — Google's BBR — to capital deployment. Instead of trading until it hits a risk limit (the loss-based behaviour of 1980s TCP), it continuously models a ceiling from measured value-rate and result-latency, paces inventory below it, and probes upward only when the model says there's room. Underneath sits a hard floor — per-position, total inventory, rolling-24h — that holds regardless of what the model believes; a test fires 10,000 retries at it and proves it cannot overspend. The transplant has precedent: Netflix took BBR's insight out of the network and into RPC concurrency limits. This is the second hop — requests to capital. Volume comes from quoting both sides continuously; survival comes from the controller. In a race scored on volume and P&L over a fixed window, the agent still standing at hour 47 wins.

Racing for

Botcamp

Nivesh Gajengi

i want to make Trading Strategy bot that can be used to backtest trades and then create stratergies to use for other agents

Racing for

Meteora

Sergiu O

Solo builder, Chisinau. Market microstructure and liquidation data. 25 services in production.

quench is a market maker for Bitget USDT-M perpetuals, built as a Hummingbot V2 controller. It quotes both sides in units of realised volatility, and every exit scales with the quote that filled it, so a fill five volatility units away from mid targets its way back toward mid instead of a fixed take profit. That single detail is what decides whether a wide quote pays for itself. On top of the quoting sits a liquidation fuel map. A collector reads open interest changes off the perpetual tape and projects them into leverage-implied liquidation clusters above and below price, marking a cluster spent once the tape has traded through it. The agent will not sell into unspent short-liquidation fuel above it and will not buy into long-liquidation fuel below it. When the feed goes stale the layer switches itself off and the agent falls back to plain volatility-scaled quoting. It never acts on stale data. I ran it inside Hummingbot's own V2 backtesting engine over fourteen days of one-minute SOL data, with 34 offline tests, and I will tell you what the numbers said rather than what I wanted them to say. Quoting one volatility unit wide loses money at a 2 bp maker fee, because gross edge per round trip is 2.3 bp. Quoting five and ten wide earns 5.7 bp gross, clears the fee, and returns a t-statistic of 2.4 across 53 fills with both halves of the sample positive. The liquidation layer as first written cost 11 percent of net, so I cut the component responsible instead of keeping it for the story. Solo builder in Chisinau, running about 25 services in production on my own box with watchdogs. The agent will still be alive at hour 47.

Racing for

Bitget

Due Diligence by Top Traders

looking for new algos for my portfolio

Racing for

Bitget

kenneth umoekpe

Regime-Aware Solana Liquidity Agent Build a Condor agent that autonomously provides concentrated liquidity on Meteora or Orca while protecting its capital from volatile or unsafe pools. The agent would: Scan eligible Solana pools. Rank them using fees, liquidity, volume, volatility, and token-risk signals. Detect whether the market is trending, ranging, or becoming unstable. Open concentrated-liquidity positions only when expected fee income justifies the risk. Dynamically widen, narrow, or reposition its liquidity range. Exit when volatility, drawdown, pool quality, or impermanent-loss risk becomes excessive. Explain and log every decision. Enforce hard capital limits independently of the LLM.

jilt jeeltcraft

creative developer

the agent operates in the omnity ree network with its own LP and flash loan infrastructure, across evm, ICP and Bitcoin networks, atomically.

Racing for

Orca

Alan Coppola

Trading agent description: This agent implements an active, tight-range concentrated liquidity market making strategy on Orca Whirlpools (Solana), targeting the SOL/USDC pair. Rather than deploying a passive full-range LP position, the agent continuously monitors price and maintains an ultra-narrow tick range (±1–2% of spot) around the current market price to maximize capital efficiency and fee capture per dollar deployed. Strategy type: Active concentrated liquidity provision with automated range-rebalancing. When price exits the current tick range, the agent closes the position and immediately reopens a new tight range centered on the updated price — converting a normally passive AMM LP position into an actively managed market making strategy. The agent also applies a directional skew (asymmetric range placement) based on short-term momentum signals, biasing liquidity toward the side price is more likely to move, which increases fee capture on directional moves while still collecting fees if price stays range-bound. Markets/exchanges: Trades exclusively on Orca Whirlpools on Solana, accessed via Hummingbot Gateway's DEX connector. Primary pair is SOL/USDC, chosen for deep liquidity, high trading volume, and Orca's 0.30% base fee tier with adaptive fee scaling during volatility spikes. What makes this unique: Most LP bots optimize for long-term stability and minimize impermanent loss through wide ranges. This agent is optimized for short evaluation windows, deliberately trading capital efficiency and IL risk for maximum fee-per-dollar throughput — effectively acting as "leverage" on a spot AMM without any borrowing, since a ±1% range can achieve over 200x the capital efficiency of a full-range position. Backtested rebalancing thresholds and range parameters are validated offline using a zipline-reloaded–based fee/IL simulation before being deployed live via Hummingbot Gateway, bridging traditional quant backtesting infrastructure with on-chain execution. Prepared with help from Perplexity Deep Research

Racing for

Orca

Kristian Mikula

I'm building an automated liquidity-management agent for Meteora's DLMM pools on Solana, using a Hummingbot Controller connected through the Gateway connector. It opens a concentrated-liquidity position centered on the current price, monitors the pool continuously, and automatically closes and re-opens the position whenever price drifts outside the active range so capital keeps earning fees instead of sitting idle out of range. I'm starting with a simple, well-tested bin distribution and conservative rebalancing thresholds to limit churn and impermanent loss, with room to add volatility-based sizing as I iterate. This is my first Solana/DeFi build, so I'm prioritizing something simple and reliable over something exotic.

Racing for

Meteora

Nirmalandu Das

I want to build an AI crypto trading agent that thinks like a skilled discretionary trader. It will trade BTC, ETH, and highly liquid altcoins by analyzing market structure, liquidity, volume, momentum, volatility, and multi-timeframe trends. The agent will adapt to changing market conditions, avoid low-quality setups, and dynamically manage position size, stop-loss, and take-profit. Its key advantage is knowing when not to trade. Every trade will have a clear reason, risk level, and invalidation point. The goal is disciplined, explainable, risk-adjusted trading not endless signals or unrealistic win-rate promises.

Half Doctor

The Derive Volatility Spread Trader is an autonomous quantitative agent that systematically harvests crypto options volatility risk premia on Derive. By combining forecast RV vs. IV edge modelling with real-time Dealer GEX intelligence from Derivatives Monkey, it executes atomic 4-leg RFQ option packages and maintains strict delta neutrality via zero-fee perpetual rebalancing.

Racing for

Derive