Gate

Ilpo Vaatainen

Hybrid Quantitative Trader

Proprietary hybrid system combining algorithmic generation with AI validation. Proven backtested approach. Risk-first architecture.

Racing for

Derive

Leo M.

I want to build an adaptive market making agent. The bot will provide two sided liquidity near the touch on liquid perp markets while dynamically controlling spread width, order size, and inventory skew based on real time volatility, directional pressure, and current position exposure. The strategy is a dynamic market maker rather than a static quoting bot. In calm conditions, it tightens spreads and increases participation to maximize fill rate and trading volume. In unstable or one sided conditions, it widens quotes, reduces size, and shifts into defense mode to avoid getting run over by adverse inventory. The agent also includes modest flow aware skewing, allowing it to lean with short term market pressure when conditions are favorable instead of blindly fading every move. The goal is to build a bot that stays active, protects capital, and earns both spread capture and leaderboard relevance over the full race window.

Racing for

Gate

Tomás Gaudino

A market maker for Orca's concentrated liquidity pools (Whirlpools, Solana) that quotes tight tick ranges right around the mid price — maximizing fee capture per unit of capital — with a portfolio-level inventory policy that governs rebalancing so the strategy never ends up fully long in a sell-off or fully short in a rally.

Racing for

Orca

I want to build an autonomous trading agent centered around market regime detection and adaptive decision-making. The agent continuously monitors BTC and broader market conditions to identify shifts between trending, ranging, high-volatility, and reversal environments. Based on the detected regime, it dynamically adjusts its trading behavior, risk parameters, and execution logic instead of relying on a single static strategy. The core edge comes from combining regime detection with rebound and reversal identification. The agent is designed to detect exhaustion moves, oversold conditions, and rapid sentiment shifts, allowing it to capture rebounds and short-term opportunities with predefined take-profit and stop-loss levels. It prioritizes a high volume of trades and fast execution while maintaining disciplined risk management. By combining adaptive learning, regime-aware trading, and rebound-capture mechanisms, the agent can remain effective across changing market conditions without being locked into a single strategy.

Racing for

Gate

Kunal Ranjan

MM

I plan to develop a perpetual market-making agent on Gate.io that optimizes liquidity provision by incorporating real-time funding rate dynamics directly into its quote-skew logic . Traditional bots only look at price risk, but my agent will dynamically adjust its bid and ask distances based on whether the current funding rate makes holding a directional inventory profitable or costly .The strategy aims to systematically lower inventory root-mean-square (RMS) exposure during intense market funding pressures while preserving robust mean final equity compared to standard MM models .

Racing for

Gate

noboru noboru

Multi-Asset Trend Follower | GRVT Perpetuals | EMA+ATR+RSI

I am building a multi-asset trend-following agent on GRVT perpetuals, targeting TradFi and altcoin pairs (XAU, NVDA, TSLA, CL, BNB, DOGE, HYPE) to maximize GRVT airdrop point multipliers (3x TradFi, 2x alts) while generating directional alpha. The agent uses EMA50/200 trend filter, ATR-based volatility confirmation, and RSI entry timing on 15-minute candles. Risk is fixed at 1.5% per trade with dynamic position sizing. Exit logic uses Triple Barrier Method: ATR-based stop-loss, take-profit, and trailing stop to let winners run. Maximum 2 concurrent positions across uncorrelated sectors to control drawdown on a small account.

Kingsley Ojilere

Accountable AI trader with on-chain proof of every decision I will build it for gate and bybit exchange AI trader that proves every decision on-chain

Kevin Chon

Senior Machine Learning Engineer

I want to build an advanced liquidity provision (LP) agent specifically for the Orca decentralized exchange, utilizing the Condor framework to automate and optimize range management. The Core Problem: Most Orca liquidity providers fail due to flawed range management strategies. They either deploy static ranges that quickly drift out of zone as market conditions evolve, or they employ reactive rebalancing logic. This reactive approach is particularly destructive: it often forces rebalances during market flushes, locking in realized impermanent loss (IL) at the exact worst possible price points. Our Solution: Signal-Driven Execution: Our agent addresses these inefficiencies by integrating external orderflow data—specifically from Binance—to gain predictive insight into market movement. We use this data to perform two critical analytical functions: Trend Anticipation: Predicting SOL directional bias to center our liquidity range more accurately. Volatility Filtering: Distinguishing between genuine trend shifts and temporary, high-volatility capitulation flushes. This signal-driven approach directly informs the agent’s execution logic. It dictates where to center our range, how to calibrate tick widths, and—most importantly—when not to rebalance. By holding positions through short-term exhaustion moves rather than panic-selling or rebalancing into volatility, we avoid the 'bleeding' effect common to reactive strategies. Implementation with Condor: We will leverage the Condor LP Executor framework to handle the lifecycle management of these positions. The Condor executors allow us to programmatically wrap our logic into dynamic management containers. This offloads the heavy lifting of position maintenance to the executor, ensuring the agent remains responsive to real-time signals while maintaining strict control over our LP architecture. The Performance Thesis: Our objective is not to eliminate impermanent loss, as CL LP inherently carries IL by design. Instead, we optimize the Net P&L equation: (Fees - Realized IL - Gas Costs). Fee Capture: We stay in range longer by centering our liquidity based on orderflow rather than historical averages. IL Mitigation: We avoid realizing IL at suboptimal price points by deferring rebalances during volatility spikes. Backtesting demonstrates significant improvements in fee generation and uptime within our range compared to standard reactive models, with the greatest outperformance occurring during periods of high market volatility. While developed for SOL-based pairs, the framework is designed to be generalized across any volatile Orca pool, including RWA pairs.

Tatiana Astahova

Safe Yield Agent A conservative trading strategy focused on preserving capital and generating stable returns through disciplined risk management, low leverage, and trading only high-probability market opportunities.

Dmitry Belaventsev

Write the People, Talk with Code

A funding-aware inventory market-making agent for Hyperliquid perp, built on Condor's agent framework. Instead of one static PMM, it runs a fleet of PMM controllers across the most liquid perp pairs and reallocates capital toward whichever pair is paying the most realized PnL per unit of volume, reading Condor's 5-minute snapshots and get_custom_info to detect regime shifts and throttle exposure when a market turns trending. The edge is the funding leg: it biases inventory toward the side funding pays it to hold, earning spread and funding together while staying near delta-neutral.

David Salas

What type of strategy will your agent use? What markets or exchanges will it trade on? What makes your approach unique? I want to build a market-making agent

We want to explore Covered call strategies on options on Derive

I'm building a delta-neutral trading agent on Derive perpetuals that combines funding rate capture with options-informed positioning. The agent dynamically adjusts spread width and inventory limits based on real-time implied volatility from Derive's options markets, using the derive_perpetual connector. Key features: - Multi-collateral margin management across ETH, BTC, and USDC to maximize capital efficiency - Portfolio margin optimization: cross-position netting to reduce margin requirements and increase deployed capital - Options data integration: reads IV surface and skew to anticipate directional pressure before it hits perps - Adaptive market-making: widens spreads during vol spikes, tightens during low-vol regimes - Risk controls: max drawdown limits, position size caps, and automatic deleveraging What makes it unique: most perp market-makers ignore options signals. By incorporating Derive's native options data into a perps strategy, the agent can front-run volatility regime changes instead of reacting to them. The multi-collateral approach lets it hold positions in the assets it trades, reducing unnecessary conversions and improving capital efficiency.

harvest vrp by selling iron condors. this way it has some defined risk approach to it, while earning yield.

awais raza

I want to build a simple trading agent so I can learn how automated trading works. My goal is to understand how a bot reads market data, follows basic rules, and makes trading decisions. I am mainly interested in learning step by step, starting with a basic strategy before adding anything advanced

I want to build a multi-factor order flow trading agent for BTC perpetual futures that combines Open Interest, Volume Delta, Liquidations, and Order Book Imbalance data into high-conviction Long and Short signals. The strategy works by scoring multiple market conditions simultaneously instead of relying on price action alone. Long signals are generated when Open Interest is increasing, aggressive buy-side Volume Delta is positive, short liquidations are accelerating, and the order book shows bullish imbalance with stronger bid-side liquidity. Short signals use the inverse conditions. The agent will use configurable weighting and threshold-based scoring so trades only execute when multiple institutional-flow signals align together. It will also integrate higher timeframe market structure and VWAP filters to avoid low-quality setups and reduce noise during sideways conditions. The system is designed for crypto perpetual futures markets, initially focused on BTC and ETH perpetuals on major derivatives exchanges. My goal is to build an adaptive, data-driven trading agent that detects real leverage-driven momentum and liquidity shifts in real time, while using strict risk management, dynamic position sizing, and automated execution through Condor.

Tonny Lopez

Algorithmic Trader & Microstructure Builder

I want to build a microstructure-driven trading agent for crypto perpetual markets. The agent will analyze order book data, liquidity zones, trade flow, imbalance, and short-term volatility to detect absorption, liquidity sweeps, and execution opportunities. The system combines high-performance data processing in Rust with a Python decision layer. Rust transforms raw market data into structured signals, while Python evaluates those signals to decide whether to enter, avoid trading, reduce exposure, or wait for better conditions. Within Condor, I want to adapt this into an autonomous agent that observes market conditions, generates microstructure signals, applies strict risk controls, and is tested through simulation or backtesting before live deployment.

Israel Ajayi

market Flow

FlowEdge Regime Adaptive Directional Trading Agent FlowEdge is a directional trading agent built on Hummingbot's V2 framework that adapts its behavior based on live market conditions. It trades crypto perpetual futures — primarily BTC-USDT, ETH-USDT, and SOL-USDT on exchanges like Binance Perpetual, Bybit Perpetual, and Hyperliquid. What it does: The agent uses two timeframes simultaneously. Fast 3-minute candles generate trading signals using Candle Flow Imbalance and VWAP deviation. Slow 15-minute candles classify the market regime using ADX into three states: ranging, trending, or extreme. Entries only fire when at least one timeframe confirms a trending regime otherwise the agent sits out entirely. When it does trade, it places three DCA maker limit orders at price levels that scale dynamically with NATR volatility. Calm markets get tight entries, volatile markets get wide entries. Stop-loss and take-profit scale the same way. What makes it unique: The agent has an embedded OODA loop — it tracks its own last 20 trades in a rolling window and adjusts its signal threshold automatically. If it starts losing, it tightens its entry criteria. If it's winning consistently, it loosens back. This self-adaptation runs every tick inside the controller with zero external dependencies no separate LLM process, no external API calls, no Redis or Kafka. It also reads live funding rates on perpetual pairs and applies a directional bias when positioning is crowded, and uses a gradual RSI dampener instead of a binary filter to preserve partial conviction on strong signals. The entire agent is a single self-contained Python file that inherits from DirectionalTradingControllerBase and uses DCAExecutorConfig with MAKER mode — the same proven pattern as dman_v3. No infrastructure setup needed beyond Hummingbot itself. Vision for the Builders Cup: For the hackathon, I plan to wrap FlowEdge Pro as a full Condor Trading Agent with an LLM-powered reasoning layer that can narrate regime changes, send Telegram alerts on state transitions, and accept natural-language parameter tuning commands. The execution layer is already production-ready the Condor wrapper adds the agentic intelligence on top.

Victor Adeleke

Market master

I'll build a trading agent that combines quantitative analysis, real-time market intelligence, and adaptive risk management to trade crypto, The agent will operate on Binance and Bybit. The strategy is a hybrid multi-factor system that combines: Trend-following models to capture medium- and long-term momentum, Mean reversion algorithms for short-term inefficiencies, The agent will analyze multiple data streams simultaneously, including price action, volatility, order-book imbalance, macroeconomic events, and sentiment signals. It will dynamically switch strategies depending on whether markets are trending, ranging, or highly volatile. What makes this approach unique is the integration of: Risk-first architecture — capital preservation is built into every trade through dynamic stop-losses, portfolio exposure controls, and volatility-adjusted sizing. Cross-market intelligence — the system identifies correlations and arbitrage opportunities between crypto markets in real time. Explainable trading signals — every trade recommendation includes a human-readable explanation of why the position was entered, improving transparency and trust.

I want to build a liquidation sniper bot on Hyperliquid and Binance

Ilpo Vaatainen

Hybrid Quantitative Trader

Proprietary hybrid system combining algorithmic generation with AI validation. Proven backtested approach. Risk-first architecture.

Racing for

Derive

Leo M.

I want to build an adaptive market making agent. The bot will provide two sided liquidity near the touch on liquid perp markets while dynamically controlling spread width, order size, and inventory skew based on real time volatility, directional pressure, and current position exposure. The strategy is a dynamic market maker rather than a static quoting bot. In calm conditions, it tightens spreads and increases participation to maximize fill rate and trading volume. In unstable or one sided conditions, it widens quotes, reduces size, and shifts into defense mode to avoid getting run over by adverse inventory. The agent also includes modest flow aware skewing, allowing it to lean with short term market pressure when conditions are favorable instead of blindly fading every move. The goal is to build a bot that stays active, protects capital, and earns both spread capture and leaderboard relevance over the full race window.

Racing for

Gate

Tomás Gaudino

A market maker for Orca's concentrated liquidity pools (Whirlpools, Solana) that quotes tight tick ranges right around the mid price — maximizing fee capture per unit of capital — with a portfolio-level inventory policy that governs rebalancing so the strategy never ends up fully long in a sell-off or fully short in a rally.

Racing for

Orca

I want to build an autonomous trading agent centered around market regime detection and adaptive decision-making. The agent continuously monitors BTC and broader market conditions to identify shifts between trending, ranging, high-volatility, and reversal environments. Based on the detected regime, it dynamically adjusts its trading behavior, risk parameters, and execution logic instead of relying on a single static strategy. The core edge comes from combining regime detection with rebound and reversal identification. The agent is designed to detect exhaustion moves, oversold conditions, and rapid sentiment shifts, allowing it to capture rebounds and short-term opportunities with predefined take-profit and stop-loss levels. It prioritizes a high volume of trades and fast execution while maintaining disciplined risk management. By combining adaptive learning, regime-aware trading, and rebound-capture mechanisms, the agent can remain effective across changing market conditions without being locked into a single strategy.

Racing for

Gate

Kunal Ranjan

MM

I plan to develop a perpetual market-making agent on Gate.io that optimizes liquidity provision by incorporating real-time funding rate dynamics directly into its quote-skew logic . Traditional bots only look at price risk, but my agent will dynamically adjust its bid and ask distances based on whether the current funding rate makes holding a directional inventory profitable or costly .The strategy aims to systematically lower inventory root-mean-square (RMS) exposure during intense market funding pressures while preserving robust mean final equity compared to standard MM models .

Racing for

Gate

noboru noboru

Multi-Asset Trend Follower | GRVT Perpetuals | EMA+ATR+RSI

I am building a multi-asset trend-following agent on GRVT perpetuals, targeting TradFi and altcoin pairs (XAU, NVDA, TSLA, CL, BNB, DOGE, HYPE) to maximize GRVT airdrop point multipliers (3x TradFi, 2x alts) while generating directional alpha. The agent uses EMA50/200 trend filter, ATR-based volatility confirmation, and RSI entry timing on 15-minute candles. Risk is fixed at 1.5% per trade with dynamic position sizing. Exit logic uses Triple Barrier Method: ATR-based stop-loss, take-profit, and trailing stop to let winners run. Maximum 2 concurrent positions across uncorrelated sectors to control drawdown on a small account.

Kingsley Ojilere

Accountable AI trader with on-chain proof of every decision I will build it for gate and bybit exchange AI trader that proves every decision on-chain

Kevin Chon

Senior Machine Learning Engineer

I want to build an advanced liquidity provision (LP) agent specifically for the Orca decentralized exchange, utilizing the Condor framework to automate and optimize range management. The Core Problem: Most Orca liquidity providers fail due to flawed range management strategies. They either deploy static ranges that quickly drift out of zone as market conditions evolve, or they employ reactive rebalancing logic. This reactive approach is particularly destructive: it often forces rebalances during market flushes, locking in realized impermanent loss (IL) at the exact worst possible price points. Our Solution: Signal-Driven Execution: Our agent addresses these inefficiencies by integrating external orderflow data—specifically from Binance—to gain predictive insight into market movement. We use this data to perform two critical analytical functions: Trend Anticipation: Predicting SOL directional bias to center our liquidity range more accurately. Volatility Filtering: Distinguishing between genuine trend shifts and temporary, high-volatility capitulation flushes. This signal-driven approach directly informs the agent’s execution logic. It dictates where to center our range, how to calibrate tick widths, and—most importantly—when not to rebalance. By holding positions through short-term exhaustion moves rather than panic-selling or rebalancing into volatility, we avoid the 'bleeding' effect common to reactive strategies. Implementation with Condor: We will leverage the Condor LP Executor framework to handle the lifecycle management of these positions. The Condor executors allow us to programmatically wrap our logic into dynamic management containers. This offloads the heavy lifting of position maintenance to the executor, ensuring the agent remains responsive to real-time signals while maintaining strict control over our LP architecture. The Performance Thesis: Our objective is not to eliminate impermanent loss, as CL LP inherently carries IL by design. Instead, we optimize the Net P&L equation: (Fees - Realized IL - Gas Costs). Fee Capture: We stay in range longer by centering our liquidity based on orderflow rather than historical averages. IL Mitigation: We avoid realizing IL at suboptimal price points by deferring rebalances during volatility spikes. Backtesting demonstrates significant improvements in fee generation and uptime within our range compared to standard reactive models, with the greatest outperformance occurring during periods of high market volatility. While developed for SOL-based pairs, the framework is designed to be generalized across any volatile Orca pool, including RWA pairs.

Tatiana Astahova

Safe Yield Agent A conservative trading strategy focused on preserving capital and generating stable returns through disciplined risk management, low leverage, and trading only high-probability market opportunities.

Dmitry Belaventsev

Write the People, Talk with Code

A funding-aware inventory market-making agent for Hyperliquid perp, built on Condor's agent framework. Instead of one static PMM, it runs a fleet of PMM controllers across the most liquid perp pairs and reallocates capital toward whichever pair is paying the most realized PnL per unit of volume, reading Condor's 5-minute snapshots and get_custom_info to detect regime shifts and throttle exposure when a market turns trending. The edge is the funding leg: it biases inventory toward the side funding pays it to hold, earning spread and funding together while staying near delta-neutral.

David Salas

What type of strategy will your agent use? What markets or exchanges will it trade on? What makes your approach unique? I want to build a market-making agent

We want to explore Covered call strategies on options on Derive

I'm building a delta-neutral trading agent on Derive perpetuals that combines funding rate capture with options-informed positioning. The agent dynamically adjusts spread width and inventory limits based on real-time implied volatility from Derive's options markets, using the derive_perpetual connector. Key features: - Multi-collateral margin management across ETH, BTC, and USDC to maximize capital efficiency - Portfolio margin optimization: cross-position netting to reduce margin requirements and increase deployed capital - Options data integration: reads IV surface and skew to anticipate directional pressure before it hits perps - Adaptive market-making: widens spreads during vol spikes, tightens during low-vol regimes - Risk controls: max drawdown limits, position size caps, and automatic deleveraging What makes it unique: most perp market-makers ignore options signals. By incorporating Derive's native options data into a perps strategy, the agent can front-run volatility regime changes instead of reacting to them. The multi-collateral approach lets it hold positions in the assets it trades, reducing unnecessary conversions and improving capital efficiency.

harvest vrp by selling iron condors. this way it has some defined risk approach to it, while earning yield.

awais raza

I want to build a simple trading agent so I can learn how automated trading works. My goal is to understand how a bot reads market data, follows basic rules, and makes trading decisions. I am mainly interested in learning step by step, starting with a basic strategy before adding anything advanced

I want to build a multi-factor order flow trading agent for BTC perpetual futures that combines Open Interest, Volume Delta, Liquidations, and Order Book Imbalance data into high-conviction Long and Short signals. The strategy works by scoring multiple market conditions simultaneously instead of relying on price action alone. Long signals are generated when Open Interest is increasing, aggressive buy-side Volume Delta is positive, short liquidations are accelerating, and the order book shows bullish imbalance with stronger bid-side liquidity. Short signals use the inverse conditions. The agent will use configurable weighting and threshold-based scoring so trades only execute when multiple institutional-flow signals align together. It will also integrate higher timeframe market structure and VWAP filters to avoid low-quality setups and reduce noise during sideways conditions. The system is designed for crypto perpetual futures markets, initially focused on BTC and ETH perpetuals on major derivatives exchanges. My goal is to build an adaptive, data-driven trading agent that detects real leverage-driven momentum and liquidity shifts in real time, while using strict risk management, dynamic position sizing, and automated execution through Condor.

Tonny Lopez

Algorithmic Trader & Microstructure Builder

I want to build a microstructure-driven trading agent for crypto perpetual markets. The agent will analyze order book data, liquidity zones, trade flow, imbalance, and short-term volatility to detect absorption, liquidity sweeps, and execution opportunities. The system combines high-performance data processing in Rust with a Python decision layer. Rust transforms raw market data into structured signals, while Python evaluates those signals to decide whether to enter, avoid trading, reduce exposure, or wait for better conditions. Within Condor, I want to adapt this into an autonomous agent that observes market conditions, generates microstructure signals, applies strict risk controls, and is tested through simulation or backtesting before live deployment.

Israel Ajayi

market Flow

FlowEdge Regime Adaptive Directional Trading Agent FlowEdge is a directional trading agent built on Hummingbot's V2 framework that adapts its behavior based on live market conditions. It trades crypto perpetual futures — primarily BTC-USDT, ETH-USDT, and SOL-USDT on exchanges like Binance Perpetual, Bybit Perpetual, and Hyperliquid. What it does: The agent uses two timeframes simultaneously. Fast 3-minute candles generate trading signals using Candle Flow Imbalance and VWAP deviation. Slow 15-minute candles classify the market regime using ADX into three states: ranging, trending, or extreme. Entries only fire when at least one timeframe confirms a trending regime otherwise the agent sits out entirely. When it does trade, it places three DCA maker limit orders at price levels that scale dynamically with NATR volatility. Calm markets get tight entries, volatile markets get wide entries. Stop-loss and take-profit scale the same way. What makes it unique: The agent has an embedded OODA loop — it tracks its own last 20 trades in a rolling window and adjusts its signal threshold automatically. If it starts losing, it tightens its entry criteria. If it's winning consistently, it loosens back. This self-adaptation runs every tick inside the controller with zero external dependencies no separate LLM process, no external API calls, no Redis or Kafka. It also reads live funding rates on perpetual pairs and applies a directional bias when positioning is crowded, and uses a gradual RSI dampener instead of a binary filter to preserve partial conviction on strong signals. The entire agent is a single self-contained Python file that inherits from DirectionalTradingControllerBase and uses DCAExecutorConfig with MAKER mode — the same proven pattern as dman_v3. No infrastructure setup needed beyond Hummingbot itself. Vision for the Builders Cup: For the hackathon, I plan to wrap FlowEdge Pro as a full Condor Trading Agent with an LLM-powered reasoning layer that can narrate regime changes, send Telegram alerts on state transitions, and accept natural-language parameter tuning commands. The execution layer is already production-ready the Condor wrapper adds the agentic intelligence on top.

Victor Adeleke

Market master

I'll build a trading agent that combines quantitative analysis, real-time market intelligence, and adaptive risk management to trade crypto, The agent will operate on Binance and Bybit. The strategy is a hybrid multi-factor system that combines: Trend-following models to capture medium- and long-term momentum, Mean reversion algorithms for short-term inefficiencies, The agent will analyze multiple data streams simultaneously, including price action, volatility, order-book imbalance, macroeconomic events, and sentiment signals. It will dynamically switch strategies depending on whether markets are trending, ranging, or highly volatile. What makes this approach unique is the integration of: Risk-first architecture — capital preservation is built into every trade through dynamic stop-losses, portfolio exposure controls, and volatility-adjusted sizing. Cross-market intelligence — the system identifies correlations and arbitrage opportunities between crypto markets in real time. Explainable trading signals — every trade recommendation includes a human-readable explanation of why the position was entered, improving transparency and trust.

I want to build a liquidation sniper bot on Hyperliquid and Binance