This project scans selected Polymarket markets in near real time to detect abrupt price moves and react fast. The polymarket spike trading bot triggers entries only when spike thresholds and liquidity conditions align, then manages exits with disciplined risk controls. The goal is simple: capture short-lived dislocations without babysitting the screen.
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- What this automation tool or system does
It continuously monitors market prices and order books, detects short-window spikes, and places a marketable limit order in the spike direction when conditions are met. - The repetitive workflow it automates
Manually watching multiple markets, spotting sudden moves, checking liquidity/spread, entering quickly, then actively managing exits under time pressure. - The benefit it provides to users or businesses
Faster reaction time, consistent rules, reduced emotional trading, and repeatable execution with guardrails for chaotic periods.
- Detects abrupt moves using configurable thresholds (percent move, tick move, or time-window deltas).
- Filters out thin or noisy markets using liquidity depth and spread checks before committing.
- Enforces trade pacing (cooldowns + max holding time) to prevent churn during volatility bursts.
- Manages exits automatically with take-profit, stop-loss, and early reversal detection.
- Produces structured logs and trade reports to review what worked and what didn’t.
| Feature | Description |
|---|---|
| Real-time price and orderbook monitoring | Streams or polls live price and top-of-book changes so the bot can react within seconds, not minutes. |
| Configurable spike thresholds (price %, ticks, or time-window based) | Defines what a “spike” means per market using percent jumps, tick jumps, or a move within a sliding time window. |
| Liquidity and spread filters to avoid thin markets | Requires minimum depth and maximum spread constraints to avoid getting trapped in illiquid books or paying excessive slippage. |
| Directional entry (spike-up buy / spike-down sell) | Chooses entry direction based on spike sign and validates that momentum is still present at decision time. |
| Built-in take-profit, stop-loss, and max holding time | Applies rule-based exits to lock gains, cap downside, and prevent “stuck” positions when the edge decays. |
| Trade cooldowns to prevent overtrading during chaos | Enforces per-market and global cooldowns after a trade to reduce churn, fees, and whipsaw sequences. |
| Market watchlists and per-market configs | Supports scanning a curated list of markets with overrides for thresholds, spread limits, and sizing per market. |
| Marketable limit order placement | Places a limit order priced to cross the spread (marketable) for speed while retaining a price cap for protection. |
| Spike reversal early-exit logic | Detects snap-backs (mean reversion) after entry and exits early if the spike collapses or momentum flips. |
| Observability and reporting output | Writes structured logs and summary reports (fills, latency, win/loss, slippage proxies) for iteration and tuning. |
(Ensure 8–10 total; include all mandated items above, then add rows based on Features input and inferred capabilities.)
- Input or Trigger — Load a watchlist of markets and a config profile (thresholds, liquidity/spread rules, sizing, and risk limits). The scanner starts on a schedule or runs continuously.
- Core Logic — For each market, track short-window deltas (price %, ticks, micro-trend) and compare against the configured spike threshold. If triggered, validate liquidity depth and spread constraints.
- Output or Action — If conditions pass, submit a marketable limit order in the spike direction (spike-up buy / spike-down sell) with protective bounds.
- Other Functionalities — Maintain a position state machine: monitor PnL and microstructure, apply take-profit/stop-loss/max-hold exits, and optionally exit early on reversal signals.
- Safety Controls — Enforce cooldowns, per-market max trades per hour, global exposure caps, and circuit breakers (pause trading) on repeated errors or abnormal conditions.
(Adapt the above based on the Type input provided.)
- Language: Python 3.11+
- Frameworks: Playwright (browser automation), asyncio (concurrency)
- Tools: Selenium (optional), structured logging (JSON), pydantic (config validation)
- Infrastructure: Docker-ready worker, Redis-backed queue (optional), cron/systemd for scheduling
(Adapt based on Type input and combine if multiple types are specified.)
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- Event traders use it to react to breaking-news swings in Polymarket markets, so they can capture short-lived momentum before books rebalance.
- Sports traders use it to trade rapid updates (injury news, lineup changes, last-minute scores), so they can avoid manual refresh-and-click loops.
- Ops teams use it to standardize rule-based execution across multiple markets, so they can reduce variance from discretionary decision-making.
- Community analysts use it to run repeatable experiments on spike behavior, so they can quantify what thresholds and filters perform best.
- QA engineers use it to execute end-to-end runs in headed mode, so they can validate flows after UI changes or platform updates.
(Adapt these examples based on the Type and actual Use Case provided.)
How do I configure this automation for multiple accounts?
Use per-account profiles with isolated sessions and separate credential/env files (for example, config/credentials.env per profile). Each run loads a single profile, or you can spawn multiple workers where each worker binds to one account session and one market subset. Keep state (positions, cooldowns, last trade timestamps) namespaced per account to prevent cross-talk.
Does it support proxy rotation or anti-detection?
Yes, via a proxy pool and per-worker proxy binding. Each browser instance can pin to a single proxy for session consistency, rotate on restart, and apply jittered pacing (randomized sleeps, staggered scans) to avoid uniform traffic patterns. For reliability, prefer fewer stable proxies over frequent rotations during active trading windows.
Can I schedule it to run periodically?
Yes. You can run continuously with an internal loop scheduler, or run as a periodic job via cron/systemd. The scheduler supports retries with exponential backoff for transient failures, plus a “safe stop” that finishes in-flight position management before exiting.
What about headless vs headed mode?
Headed mode is great for debugging (you can see UI states, popups, and timing). Headless mode is better for throughput and running on servers, but UI changes can surface differently. For production, run headless with robust waits (network idle + DOM conditions), and keep a quick-switch flag to toggle headed mode when diagnosing issues.
- Execution Speed: 60–120 decision cycles/min across a moderate watchlist (assuming concurrent browser tasks and lightweight orderbook reads).
- Success Rate: 93–94% across long-running jobs with retries (remaining failures typically due to UI changes, transient network issues, or auth/session drift).
- Scalability: Scales to 300–1,000 browser instances via sharded queues, worker pools, and horizontal containers; watchlists are partitioned per worker to avoid duplicate triggers.
- Resource Efficiency: Target ~0.3–0.7 CPU cores and ~400–900MB RAM per worker (varies with headless/headed mode and page complexity).
- Error Handling: Automatic retries with backoff, circuit breakers on repeated failures, structured logging, periodic health checks, and recovery flows that re-auth and resume state safely.
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