polymarket-elon-tweetsTrade Polymarket "Elon Musk # tweets" markets using XTracker post count data. Buys adjacent range buckets when combined cost < $1 for structural edge. Use wh...
Install via ClawdBot CLI:
clawdbot install adlai88/polymarket-elon-tweetsTrade "Elon Musk # tweets" markets on Polymarket using XTracker post count data.
Use this skill when the user wants to:
Polymarket runs weekly "How many tweets will Elon post?" events with range buckets (e.g., 200-219, 220-239, 240-259). Exactly one bucket resolves YES = $1. The strategy:
When user asks to install or configure this skill:
pip install simmer-sdk
SIMMER_API_KEYWALLET_PRIVATE_KEY| Setting | Env Variable | Config Key | Default | Description |
|---------|-------------|------------|---------|-------------|
| Max bucket sum | SIMMER_ELON_MAX_BUCKET_SUM | max_bucket_sum | 0.90 | Only buy if cluster prices sum < this |
| Max position | SIMMER_ELON_MAX_POSITION | max_position_usd | 5.00 | Maximum USD per bucket |
| Bucket spread | SIMMER_ELON_BUCKET_SPREAD | bucket_spread | 1 | Neighbors on each side (1 = 3 buckets) |
| Smart sizing % | SIMMER_ELON_SIZING_PCT | sizing_pct | 0.05 | % of balance per trade |
| Max trades/run | SIMMER_ELON_MAX_TRADES | max_trades_per_run | 6 | Maximum trades per scan cycle |
| Exit threshold | SIMMER_ELON_EXIT | exit_threshold | 0.65 | Sell when bucket price above this |
| Slippage max | SIMMER_ELON_SLIPPAGE_MAX | slippage_max_pct | 0.05 | Skip trade if slippage exceeds this |
| Min position | SIMMER_ELON_MIN_POSITION | min_position_usd | 2.00 | Floor for smart sizing (USD) |
| Data source | SIMMER_ELON_DATA_SOURCE | data_source | xtracker | Data source (xtracker) |
Config priority: config.json > environment variables > defaults.
# Check account balance and positions
python scripts/status.py
# Detailed position list
python scripts/status.py --positions
API Reference:
https://api.simmer.marketsAuthorization: Bearer $SIMMER_API_KEYGET /api/sdk/portfolioGET /api/sdk/positions# Dry run (default โ shows opportunities, no trades)
python elon_tweets.py
# Execute real trades
python elon_tweets.py --live
# With smart position sizing (uses portfolio balance)
python elon_tweets.py --live --smart-sizing
# Show XTracker stats only
python elon_tweets.py --stats
# Check positions only
python elon_tweets.py --positions
# View config
python elon_tweets.py --config
# Update config
python elon_tweets.py --set max_position_usd=10.00
# Disable safeguards (not recommended)
python elon_tweets.py --no-safeguards
# Quiet mode โ only output on trades/errors (ideal for cron)
python elon_tweets.py --live --quiet
# Combine: frequent scanning, minimal noise
python elon_tweets.py --live --smart-sizing --quiet
Each cycle the script:
sdk:elon-tweets for trackingIf tweet count markets aren't on Simmer yet, the skill automatically imports them:
With --smart-sizing, position size is calculated as:
sizing_pct)Before trading, the skill checks:
Disable with --no-safeguards (not recommended).
All trades are tagged with source: "sdk:elon-tweets". This means:
๐ฆ Simmer Elon Tweet Trader
==================================================
โ๏ธ Configuration:
Max bucket sum: $0.90
Max position: $5.00
Bucket spread: 1 (center ยฑ 1 = 3 buckets)
Exit threshold: 65%
Data source: xtracker
๐ XTracker Stats:
Tracking: Elon Musk # tweets Feb 13 - Feb 20
Current count: 187 posts
Pace: 243 projected
Days remaining: 2.3
๐ฏ Target cluster: 240-259 (center) + 220-239, 260-279
240-259: $0.35
220-239: $0.22
260-279: $0.18
Cluster sum: $0.75 (< $0.90 threshold) โ
Executing trades...
โ
Bought 14.3 shares of 240-259 @ $0.35
โ
Bought 22.7 shares of 220-239 @ $0.22
โ
Bought 27.8 shares of 260-279 @ $0.18
๐ Summary:
Events scanned: 2
Clusters evaluated: 2
Trades executed: 3
Exits: 0
"No XTracker trackings found"
"Cluster sum $X.XX exceeds threshold"
"No matching Simmer markets found"
"Safeguard blocked: flip-flop warning"
"External wallet requires a pre-signed order"
WALLET_PRIVATE_KEY is not set in the environmentexport WALLET_PRIVATE_KEY=0x"Balance shows $0 but I have USDC on Polygon"
0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174) โ not native USDC"API key invalid"
Generated Mar 1, 2026
This skill enables automated trading on prediction markets tied to Elon Musk's tweet frequency, leveraging real-time data from XTracker. Users can deploy it to capitalize on mispriced buckets in Polymarket's weekly events, executing trades when the combined cost of adjacent buckets falls below a threshold. It's ideal for individuals or small funds looking to automate speculative bets based on public figure behavior.
Institutional traders or hedge funds can use this skill to add a niche, low-correlation asset class to their portfolios by betting on social media metrics. By integrating with Simmer's SDK, it allows for systematic entry and exit based on projected tweet counts, helping diversify risk away from traditional markets. This scenario suits firms exploring alternative data strategies for alpha generation.
Universities or training programs in finance and data science can employ this skill to teach students about prediction markets, arbitrage opportunities, and automated trading systems. Students can run dry runs to analyze market inefficiencies and learn how to configure risk parameters like bucket spread and exit thresholds in a controlled environment.
Media companies or influencers focused on crypto and tech news can integrate this skill to create interactive content, such as live-trading streams or analyses of Elon Musk's tweet impact. By automating trades based on XTracker data, they can engage audiences with real-time demonstrations of market reactions to social media activity.
Professional traders managing event-driven strategies can use this skill to automate positions in tweet count markets, applying safeguards like slippage limits and exit thresholds to mitigate losses. It helps in scaling operations by handling repetitive tasks like data fetching and order execution, allowing focus on higher-level strategy adjustments.
Offer tiered subscriptions for access to enhanced features of the skill, such as premium data feeds from XTracker, advanced analytics, or priority support. Revenue is generated through monthly or annual fees, targeting active traders and institutions seeking reliable automation tools for prediction markets.
Implement a fee structure where a percentage of profits from automated trades is collected, aligning incentives with user success. This model appeals to users who prefer paying only when the skill generates returns, reducing upfront costs and encouraging adoption among risk-averse traders.
License the skill as a white-label product to other prediction market platforms or financial services, allowing them to offer Elon tweet trading as a feature. Revenue comes from licensing fees or revenue-sharing agreements, expanding reach without direct user acquisition costs.
๐ฌ Integration Tip
Ensure secure handling of private keys by using environment variables and consider implementing a cron job for scheduled runs to automate trading cycles without manual intervention.
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