Registered Agent Builders

KOMARI Subheeksh

india

XRPL AMM liquidity agent built on Condor's xrpl_market_maker scaffold. Deploys the $800 into the XRP/RLUSD AMM pool with an automated fee-compounding loop (re-adds accrued fees every N ledgers) and a passive CLOB quote layer that offers inside the AMM curve on both sides. Hard risk controls: halt on –3% drawdown, inventory bounded ±$200, no leverage, no directional signals. Volume is generated by counterparty swaps against the LP position and by CLOB fills, not by taking spread. Goal is high turnover-per-dollar with variance clamped near zero — the same shape as Cohort 13's winning agent, adapted to XRPL's near-zero fee environment where $800 is not thin capital.

Racing for

XRPL

Southen_

Remote

Strategy Type & Protocol: An autonomous, volatility-adaptive Concentrated Liquidity (DLMM) market-maker on Solana via Meteora DLMM pools, connected through Hummingbot Gateway with cross-venue delta-hedging on perpetual markets (e.g. Bitget / Gate / Hyperliquid). Core Mechanics: Rather than deploying static bin ranges, the agent dynamically prices bin spreads in Realized Volatility units ( σ t σ t ​ ). It switches between symmetric Gaussian Curve distributions during mean-reverting consolidation (maximizing fee capture per dollar) and momentum-skewed BidAsk distributions during directional flow. Cross-Venue Hedging (Delta-Neutral Yield): As price moves across bins and spot inventory shifts, the controller continuously tracks net portfolio delta ( Δ n e t Δ net ​ ) and executes low-latency micro-hedges on perpetuals to maintain delta neutrality, transforming concentrated LPing into a pure fee-harvesting engine insulated from token drawdown. What Makes This Unique: Volatility-Engineered Bins: Dynamic bin range expansion/compression calibrated to expected bin dwell time rather than arbitrary fixed percentages. Economic Churn Gate: Prevents the "rebalance whip" by requiring E [ Incremental Fees ] > Slippage + Solana Priority Fees + Hedge Rebalance Cost E[Incremental Fees]>Slippage+Solana Priority Fees+Hedge Rebalance Cost, backed by a multi-slot dwell verification. Fail-Closed Safety Engine: Built with Hummingbot V2 Controller architecture, including automated markout telemetry, stale RPC circuit breakers, and hard inventory floor stops.

Racing for

Meteora

memeshe

Da Nang, Viet Nam

I want to build an CLOB↔AMM dual-venue market maker on XRPL.

Racing for

XRPL

ace

Anambra, Nigeria

I want to build BlackBox an autonomous LP risk engine for Meteora. It doesn't blindly chase fees. It predicts when liquidity becomes the wrong position, simulates alternatives, and moves - or retreats - before the market forces it to.

Racing for

Meteora

Cedar

I am building directional strategy

Piotr Wasiel

Vibe-Quant-Trader

Żywiec, Poland

I want to build a market-neutral spot–perp basis agent on Bitget. It will monitor multiple liquid markets, identify unusually wide basis relative to a rolling anchor, and open delta-neutral long spot / short perp positions when the expected basis capture and funding outweigh fees and execution costs. The agent will dynamically select markets and allocate capital based on net executable edge, while using passive spot orders with immediate perp hedging to control slippage and directional risk.

Racing for

Bitget

Luna

Malaysia

I want to build market making agent

Mirasol

Singapore

Market making agent

roux

Philippines

hedge strategy

Golden

Japan

directional agent

Elle

Singapore

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

Racing for

Bitget

ctrader xt

Philippines

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

Africa , nigeria

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

UAE

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.

Chisinau, Moldova

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

UAE, Dubai

looking for new algos for my portfolio

Racing for

Bitget

kenneth umoekpe

nigeria

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

Modena Italy

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

Portland, OR

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

Hungary

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

United Kingdom of Great Britain and Northern Ireland (the)

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

DeFi Sentinel Trading Agent — an autonomous market-making agent built on Condor that provides two-sided liquidity on Solana DEXs (Orca Whirlpools) with signal-driven range management. The agent reads on-chain order flow and volatility metrics to dynamically adjust spread width, inventory skew, and rebalancing timing. Unlike static LP bots, our agent uses anomaly detection (Sentinel-style) to avoid realizing impermanent loss during volatility flushes, while maximizing fee capture during stable ranges. Built on our MCP/agent stack (DeFi monitoring + Hummingbot execution layer).

Fikan Ali

Full-stack developer

Nigeria

I want to build an adaptive trading agent for Bitget BTC/USDT and ETH/USDT that dynamically switches between high-volume market making, funding-rate capture, and perp-basis opportunities based on real-time volatility, liquidity, inventory, and cross-market conditions. Its edge is adaptive execution: rather than using static parameters, it continuously adjusts spreads, exposure, and strategy selection to maximize risk-adjusted trading volume while protecting capital.

Racing for

Bitget

Anonymous Builder

Dallas

build a liquidity monitor curator for megaeth & if possible include other chains w/ similar settlement to provide signals for arbitrage opportunities; identify cryptotokens that behave more like cryptocurrency & cryptocurrencies that behave more like cryptotokens.

Racing for

Botcamp

Soumalya Paul

India,Bangalore Urban

I'm building an autonomous liquidity-management agent for Meteora's DLMM pools. It can be deployed in two forms: as a vault that accepts user deposits and manages multiple pools together, or as a liquidity-management contract that handles any single pool. In both cases the agent actively manages liquidity by dynamically structuring bin allocations around the current price. Depending on market conditions, it shifts between a delta-neutral configuration (symmetric bins plus hedging to minimize directional exposure) and a profit-seeking configuration (skewed, tighter bins to capture more fees during range-bound or trending conditions). It continuously rebalances bins based on volatility, price movement, and fee generation to reduce impermanent loss while maximizing yield for depositors.

Racing for

Meteora

riyan

Indonesia

Darwin Trader is an adaptive autonomous trading agent for crypto perpetual markets. It continuously runs multiple deterministic strategies in a live Shadow Arena, including momentum, mean reversion, liquidity vacuum, funding crowding, and volatility breakout. Darwin can switch between existing strategies, safely adjust strategy parameters within predefined bounds, and generate new bounded strategy variants. Every new or mutated strategy must first prove itself in shadow trading before progressing through Challenger → Probation → Active and receiving live capital. An event-driven AI council evaluates regime changes and strategy performance, while hard risk limits and Hummingbot handle deterministic execution, position management, stop-losses, take-profit, and order lifecycle. The initial target is Gate perpetual markets. What makes Darwin unique is that the LLM never directly places trades. Strategies compete on measurable live-market performance, and only strategies that demonstrate superior risk-adjusted fitness can control real capital.

Racing for

Gate

Crypto T

Building a multi-layer autonomous trading system on Bitget USDT-M perpetuals using Hummingbot V2. Two engines run side by side on the same account: Engine 1 — Systematic Strategy: Scans a 24-pair universe across crypto, commodities (gold/silver perps), and stock perps (NVDA, TSLA, META, etc.). Three independent signal engines vote on each symbol — momentum, mean reversion, and multi-timeframe confluence. Ranks all pairs by confidence and opens up to 3 positions with per-symbol leverage (15× BTC, 10× SOL, 5× stocks). Triple barrier exits with trailing stops, hard SL/TP, and time limits. Engine 2 — Autonomous AI Agent: An LLM-powered agent that scans every 60 seconds with its own 3 position slots (6 total across both engines). Makes independent trading decisions based on market data, but also monitors and can close the strategy's positions if market conditions change. Decisions are journaled with reasoning. Uses Condor (Hummingbot's Telegram AI agent) for the loop infrastructure and decision management. What makes it unique: the two engines complement each other — the strategy handles systematic coverage across 24 pairs while the agent handles opportunistic trades and risk oversight. The agent can act as a risk manager, closing strategy positions when its analysis disagrees. All trades from both engines share a unified journal with source tags. Built with Hummingbot on Bitget. Custom dashboard with live scanner, strategy radar, trade journal, and Condor AI integration. Already live and trading.

Racing for

Bitget

I want to build a market-making agent that provides liquidity on Hyperliquid perpetuals. My strategy will use dynamic spread adjustment based on volatility and inventory levels...

Federico Cardoso

Argentina

I want to build a set of simple directional strategies that an agent backtest and deploy based on market conditions on a perpetual exchange.

Racing for

Gate

David Solutions

AI engineer

Portugal

I want to build an automated liquidity provisioning agent for ORCA

Racing for

Orca

Nolan

Search for high volatility trades and capturing the movement on trend

Macro Wang

Hangzhou

I want to build a market making agent that provides liquidity on hyperliquid perpetuals.

Racing for

Gate

Jordan Jones

Springdale, United States of America (the)

I want to build a multi-strategy autonomous trading agent for Meteora that combines CLMM liquidity provision with cross-venue arbitrage detection, all driven by LLM reasoning. Strategy: The agent runs two coordinated layers: - LP Layer — Concentrated liquidity positions on Meteora DLMM pools. Scans trending pools, ranks by fee yield, dynamically adjusts range width based on volatility, and rotates capital through per-slot take-profit/stop-loss (20%). - Arbitrage Layer — Monitors price discrepancies between Meteora, Orca, Raydium, and Hyperliquid. When the LP layer identifies a pool with sufficient depth, the agent can execute arbitrage trades that profit from venue price gaps while simultaneously improving its LP position's fee capture. What makes it unique: Most hackathon agents pick one strategy. This agent uses LLM reasoning to decide when to LP and when to arb — reading market conditions, fee rates, and pool depth to allocate capital to the highest-yield activity at any moment. The Condor harness separates the reasoning (LLM decides) from execution (Hummingbot places orders), so the agent never misses an arb window while thinking. Built on GenTech's existing multi-chain infrastructure — x402 payments, ERC-8004 identity, and gasless settlement via Q402 — this agent is designed to scale beyond the hackathon into a fully autonomous DeFi operator. Venue: Meteora DLMM (primary), Orca/Raydium (rotation), Hyperliquid (arb detection)

Racing for

Meteora

俊华 陆

Guangzhou, Guangdong, CHN

ABCMM — a market-making agent for XRP/RLUSD on Gate.io (CEX) and XRPL native DEX, run via Condor (LLM-driven decision layer) over Hummingbot execution. Core differentiator: quotes are anchored to Flare's FTSO v2 fair-value oracle (decentralized on-chain price), not the CEX mid that most other builders will reference. This means our quotes are honest relative to on-chain truth, not reflexive to the same feed. Strategy: dynamic spread = f(FTSO mid vs CEX mid divergence, inventory skew toward 50/50 XRP/RLUSD, ATR volatility). Order sizes scale with inventory distance from target. Risk guards: max absolute inventory, max order size, kill-switch if FTSO staleness > 30s, hard daily PnL stop. Plan: ship Gate first (easier infra, proven CEX connector), then mirror the same strategy to XRPL DEX — same code path, two venues. Why I can build it: 2+ years Web3 / smart-contract engineering. Recently shipped a CC-enclave rebalancer on Coston2 (Flare testnet) and a Circle Agent Stack–powered Aave keeper — both with on-chain attested execution. Same primitive pattern, now applied to live trading.

Yezir Hasan

Lagos Nigeria

An agent capable enough to win on any tracks mostly I am targeting the robin hood chain

Berg 1ce

mm learner for this compete thx！thx！thx！thx！thx！thx！thx！thx！thx！thx！thx！thx！thx！

Aditya Dargan

New York

Most Orca LPs lose money to bad range management — they sit in static ranges that drift out of zone, or they rebalance reactively right at the worst price, locking in losses at every flush. We read orderflow from Binance to do two things: anticipate where SOL is heading and distinguish a real trend from a capitulation flush. The flow signal drives where we center our range, how tight we make our ticks, and crucially when not to rebalance — holding through exhaustion moves that reactive strategies bleed into. Backtest shows X% more fees and Y% less time out of range vs. reactive rebalancing, with the biggest outperformance during volatile flushes. The framework generalizes to any volatile Orca pool, including RWA pairs Our strategy doesn't eliminate impermanent loss — every CL LP carries IL by design. What it does is (a) capture more fees by staying in range longer, and (b) avoid realizing IL at the worst possible prices by not rebalancing into flushes. Net P&L = fees − realized IL − gas. We win on the fees side and we win on the timing of the IL realization —-- Submission for registration for AgentCup: Project Vision: Intelligent Orca Liquidity Provision via Condor LP Executors My team is building 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.

Racing for

Orca

Ilpo Vaatainen

Hybrid Quantitative Trader

Helsinki

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

Racing for

XRPL

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

Mar del Plata, Argentina

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

Mohammed Zaid

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

India

A funding-aware perpetual market-making agent for Gate.io, built as a Condor agent on Hummingbot. It provides high-volume liquidity on top BTC/ETH/SOL-USDT perps, with quoting that adapts to real-time market and funding conditions rather than price risk alone. Disciplined inventory and risk controls keep exposure bounded through volatile and high-funding regimes, with the goal of robust risk-adjusted returns over a fully autonomous run. Built on a market-making engine already hardened through extensive live multi-pair testing.

Racing for

Gate

noboru noboru

Event-Aware AI Trading Agent | Liquidity + Momentum + Risk Control

Japan

I am building a volatility-adaptive multi-asset market making controller for BTC, ETH, SOL, and selected altcoins on sponsored exchanges The strategy uses EMA50/200 trend filters, ATR-based volatility bands, RSI divergence filters, inventory skew control, dynamic position sizing, and strict stop-loss rules Risk per trade is capped at 1.5%, with a maximum of 2 concurrent positions and a global drawdown stop

Kingsley Ojilere

Lagos, Nigeria

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

Seattle, Washington

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

indonesia

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

Novokuznetsk, Russian Federation (the)

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

Abuja, Nigeria

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

IBRAHIM ABDULKARIM

I want to build trading agent that just wins money

Bibhu padhy

a dev

india

I want to build a RSI based strategy where i will have a set of taken which i will going to watch and i will trade (short/long) when it reach 60-40 levels Strategy is very simple. Long when RSI close above 60 being over sold means coming out of 40 levels and for short exactly opposite. take short when it coming out of 60 and close below 40 on a given timeframe. for confirmation i am taking a next bigger time frame like if main time frame is 15m then i am taking 1h form confirmation so if its a long call then i check on confirmation time frame is it above 50 on snapshot not waiting for the candle close if short call then below 50. for Exit if its a long call i put the SL at the previous candle low and for Short Exit previous candle high

Kaira Zambo

I want to build a market-making agent that provides liquidity on XRPL via XRPliquid. My strategy is called Delta Raptor which is an autonomous AI market maker that tracked the volume acceleration of 6 pairs on hourly basis thru a routine. The Agent will inspect the report of routine and then provide liquidity on the top 2 pairs that have the highest volume gained at last hour. Delta Raptor will also have a risk management feature called price band which will not allow order placement if the price suddenly drops or exceeds 2% from starting price. It will have an Auto Rebalancing feature that will trigger whenever an asset has 60% or more. To minimize LLM cost, Deepseek is implemented thru PydanticAI.

ac wq

我将要构建的交易代理命名为 **“PerpStrat-X”** ，一个专为加密货币**永续合约**设计的多策略自适应交易系统。它的核心设计理念不是寻找圣杯般的单策略，而是让多种低相关性的子策略动态配合，同时在**资金费率、市场微观结构和链上情绪**等永续合约特有维度上建立优势。 --- ### 1. 交易市场与交易所选择 代理将部署在以下市场，兼顾流动性、去中心化选项和低延迟： - **中心化交易所（主战场）** Binance Futures、Bybit USDT Perpetual、OKX Perpetual Swap 选择理由：USDT或USDC本位永续合约流动性最好，交易对全面，API稳定，支持 WebSocket 实时行情和多种高级订单（止损限价、冰山委托等）。 - **去中心化永续协议（辅助套利与备选）** Hyperliquid、dYdX v4、GMX（V2） 理由：链上永续合约提供了不同于 CEX 的流动性池定价，经常出现与 CEX 的价格偏离，这构成独特的套利窗口。同时，交易记录完全链上，有利于策略透明化回测。 代理会同时维护多个交易所的账户和仓位，并通过统一的内部行情总线（price bus）对跨所价差、资金费率差异、深度不平衡做实时监控。 --- ### 2. 核心策略矩阵（四引擎结构） 整个代理由四个相互独立的子策略引擎构成，顶层有一个动态资本分配器决定各引擎的资金权重。 #### ① 自适应趋势追踪引擎（Adaptive Trend） - **逻辑**：使用 Donchian 通道突破 + 波动率调整移动平均（VIDYA），捕捉 1h~4h 级别趋势。 - **永续合约特化**：利用**资金费率乘数**过滤信号。当趋势方向与资金费率方向一致时，增加头寸（说明趋势有真实买盘支撑）；当价格新高但费率极端负值（空头拥挤）时，则只会轻仓跟随，避免轧空回调伤害。 - **退出机制**：结合跟踪止损和波动率目标仓位，每日根据 ATR 调整合约张数，恒定风险预算。 #### ② 资金费率回归/爆发引擎（Funding Rate Mean-Reversion & Trap） - 监控所有交易对永续合约的 8 小时资金费率 Z-score。 - **均值回归模式**：当资金费率处于历史极值（比如高于 +0.1% 或低于 -0.1%），且价格与费率背离（费率极高但价格滞涨，费率极负但价格止跌），发出反向信号，做市式入场赚取费率回归正常和价格反弹的双重利润。 - **资金费率陷阱规避**：如果资金费率极高，但持仓量仍在飙升、多空比继续上升，模型会判定为“资金费率陷阱”——此时不做反转，甚至配合趋势引擎加仓。这是避免盲目套费率爆仓的关键。 - **费率爆发模式**：当新上币或事件导致费率在短时间内巨幅波动，代理会利用期权式思维，做多波动率。例如同时在两个方向上部署突破挂单，赚取价格在费率极端化后的剧烈运动。 #### ③ 统计套利 & 板块配对引擎（Statistical Arb Pairs） - 针对高度相关资产（BTC/ETH、SOL/AVAX、L2 代币对等），使用卡尔曼滤波动态估计对冲比率，构建平稳的价差序列。 - 入场：价差超过 2 个标准差且资金费率差异不会对冲掉预期利润时，做多相对低估永续合约、做空高估合约，保持严格市场中性。 - 独特之处：配对组合会实时计算**跨资金费率成本**。例如做多低资金费率合约，做空高资金费率合约，若持仓时间预期较长，资金费率差可能构成稳定 alpha，而非成本，模型会主动选择这种“顺费率”配对方向。 #### ④ 链上事件 & 订单流驱动引擎（On-chain & Flow Alpha） - 监控链上大额转账（巨鲸运动）、交易所钱包余额变化、流动性池的突然增/减。 - 同时，利用交易所 WebSocket 深度快照，计算**订单簿不平衡指数（OFI）**和**毒性流指标（VPIN）**。 - 当检测到某永续合约突然出现强烈的买方或卖方不平衡，并且链上有对应的大额稳定币/代币转移时，发起动量狙击交易，持仓时间在分钟级，追求捕捉信息扩散前的瞬时价差。 - 此引擎特别适用于 Hyperliquid 等链上协议，其订单簿透明，可直接分析地址行为。 --- ### 3. 方法论独特之处（三大差异点） #### ▍差异化一：资金费率态势感知与动态资本分配 绝大多数代理要么忽视资金费率，要么将其作为独立套利信号。PerpStrat-X 构建了一个 **Funding Regime Classifier（资金费率体制分类器）**，把市场分为四种状态： - 趋势顺费率 - 趋势逆费率 - 费率陷阱 - 费率回归 顶层动态分配器（Bayesian 权重模型或基于近期表现的滑窗夏普最优化）会根据当前体制，调整四个子引擎的风险预算。例如，趋势逆费率阶段，趋势引擎降权，反转引擎提权。这种上下文感知能力极大降低传统策略在极端费率环境下的回撤。 #### ▍差异化二：跨 CeFi-DeFi 实时价值捕获 代理不仅交易单交易所，而是作为一个跨市场参与者，持续扫描 Binance 与 Hyperliquid 之间的永续合约价差。当价差覆盖滑点和提币/操作成本后仍有利润，它会同时在两边建立相反头寸，等价差收敛时平仓，或通过资金费率差长期持仓套取费率时间价值。这是纯粹的 delta 中性策略，为整体组合提供非方向性收益。 #### ▍差异化三：分层强化学习执行与微观结构感知 在下单层面，不使用简单的市价/限价，而是嵌入了一个轻量级 **Soft Actor-Critic 执行智能体**。它在每个时刻根据当前订单簿、价差、近期成交率，动态选择挂单激进程度、是否拆单、是否伪装成冰山订单。训练目标是最小化交易侵蚀 alpha 的冲击成本。执行层还包含“毒性回避”——当市场微观结构出现高频做市商撤退迹象时，代理会主动暂停交易，等待流动性恢复，这在永续合约的插针行情中能救命。 --- ### 4. 全流程风控框架 每个子策略都有以下硬约束，并在代理总控层面汇总： - **最大总杠杆**：动态，根据当前组合波动率和相关性自动计算，通常不超过 3 倍名义杠杆。 - **单币种风险上限**：名义敞口不超过总权益的 20%。 - **策略熔断**：单日、单周亏损达到阈值，对应子引擎自动降权或暂停。 - **资金费率监控**：若总仓位需支付的 8 小时资金费超过预期每日收益的 30%，强制部分减仓。 - **交易所风险分散**：永不将所有保证金存放于单一交易所或协议，使用 API 只读权限与提币限制。 --- 总而言之，PerpStrat-X 不是一个寻找神奇指标的代理，而是一个能理解**永续合约资金费率内部逻辑**、在**中心化和去中心化市场之间架起桥梁**，并通过**微观执行智能体**保护利润的综合交易系统。其最终目标是，在各种市场结构中都能实现稳健、低回撤的风险调整后收益。

I want to build a trading agent that exploits price drift risk on Hyperliquid derivatives during pre-market trading when price deviates from fair value during market close periods.

MJ Lee

वयधम्मा सङ्खारा अप्पमादेन सम्पादेथ वयधम्मा सङ्खारा अप्पमादेन सम्पादेथ

Ziru Niu

I want to build an order flow-driven market making agent on crypto perpetual futures. The strategy uses dynamic spread adjustment based on short-term volatility (ATR), inventory skewing via CVD and DOM imbalance signals to manage directional exposure, and a hard inventory limit with batch hedging to control drawdown. The goal is a smooth, low-drawdown equity curve through passive liquidity provision rather than directional speculation.

I am a complete beginner in algorithmic trading and I want to build my first market-making trading agent using Hummingbot. I plan to focus on providing liquidity on Dex and Cex perpetuals. My strategy will start simple with basic bid-ask spread management, then gradually add dynamic spread adjustments based on market volatility and my current inventory levels to control risk. I want to learn how to properly manage inventory, avoid big losses, and earn from the spread while trading on a fast decentralized perpetuals exchange. I’m excited to join Botcamp to learn from the instructors and improve.

hula hoops

perpetual_noob

i do not know anything and hope to learn in this process.

Николай Тараданов

1. How to deploy 'deploy'. Right now, even following the instructions doesn't work. 2. Understand the principles of orchestrating multiple bots. 3. Get a backtesting tool. 4. Write an algorithm for removing liquidity from a range.

I would like to build a binary trading agent that learns and constantly improves how to predict the probability of a buy or sell. This will be used to achieve a certain target profit, and win-rate on a daily or regular time scale.

Steven Hudspeth

Cross-venue XRP market maker. Primary venue: XRP/RLUSD on the XRPL native DEX, using Hummingbot order-book strategies with adaptive spread tuned by FTSO v2 fair-value reference. Inventory managed in FXRP and stables on Flare for hedging and yield. Built on top of FlareForward's deployed Apex trading platform on Flare Mainnet. Risk discipline: probe-mode sizing graduates to full bankroll only after live calibration metrics clear. Optional Hyperliquid perp hedge for directional risk control. Stretch goal: XRPL-native control via Flare Smart Accounts so XRPL holders can fund and operate the agent entirely from XRPL.

TANMAY SAYARE

DEVELOPER

I don't have it right now, but I will create a new one and build it .

violain Ot

I want to build a multi-exchange perpetual contract fee arbitrage strategy, including CEX and DEX,

I would like to build various types of ai agents. One example is one which can find gaps between Binance(XRP/RLUSD) and XRPL(XRP/RLUSD) pairs and trade the gaps. One that can measure order flow analysis on the XRPL accurately to take advantage of gaps.

Michael Feng

Botcamp Instructor

Sunnyvale, USA

I'll productionize the Solana memecoin LP scalping strategy I've shown in podcast episodes.

Racing for

Botcamp

hype maxi

An optimized borrowing strategy for creating liquidity events for Derive traders. This strategy leverages Derive's unique and underused favorable lending rate. It pairs it with a long-term short call and a long put on a portfolio to hedge against downside while still unlocking liquidity for personal or investment uses.

Racing for

Derive