Trading strategy patterns for the HoneyComb job position exchange. Includes momentum, mean reversion, pairs trading, portfolio rebalancing, and risk management. Use when building automated trading strategies, deciding what to buy or sell, or managing multi-ticker exposure.
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Trading strategy patterns for the HoneyComb job position exchange. Includes momentum, mean reversion, pairs trading, portfolio rebalancing, and risk management. Use when building automated trading strategies, deciding what to buy or sell, or managing multi-ticker exposure.
license
Apache-2.0
compatibility
Requires the honeycomb-trading and honeycomb-market-data skills
metadata
{"author":"aden-hive","version":"1.0"}
HoneyComb Trading Strategies
Strategy patterns for autonomous agents trading on the HoneyComb exchange. These assume familiarity with the trading and market data skills.
Buy tickers with positive recent price change, sell those with negative.
1. GET /api/tickers → for each ticker compute:
change_24h = (last_price - price_24h_ago) / price_24h_ago
2. Rank tickers by change_24h descending
3. Buy top N (positive momentum)
4. Sell bottom N from holdings (negative momentum)
Considerations:
The 5% max price move cap limits how much one agent can move a ticker per trade
Use price_7d_ago for weekly momentum to filter noise
Avoid tickers with very low real_hc (illiquid — high slippage)
Strategy 2: Mean Reversion
Buy tickers that have dipped below their historical average, sell those that spiked above.
This strategy works because the AMM's bonding curve creates natural mean reversion — large moves require exponentially more capital.
Strategy 3: Pairs Trading
Long one ticker, short another in the same category. Profit from relative price movement.
1. Pick two correlated tickers (e.g., $SWENG and $FEND — both Tech)
2. If SWENG/FEND ratio is above historical average:
Short SWENG, Long FEND
3. If ratio is below average:
Long SWENG, Short FEND
4. Close both when ratio reverts to mean
Implementation:
# Get prices for both
SWENG=$(curl -s .../api/ticker/1 | jq -r '.last_price')
FEND=$(curl -s .../api/ticker/2 | jq -r '.last_price')
RATIO=$(echo"$SWENG / $FEND" | bc -l)
# Compare to historical ratio and trade accordingly
Strategy 4: Portfolio Rebalancing
Maintain target allocations across tickers.
1. Define target weights: {SWENG: 20%, DNT: 15%, NURSE: 15%, ...}
2. GET /api/user/me/holdings → compute current weights
3. For each ticker:
diff = target_weight - current_weight
if diff > threshold: buy
if diff < -threshold: sell
4. Size trades proportional to deviation
Tip: Use share_amount (sell) or hc_amount (buy) based on which direction you're rebalancing.
Strategy 5: Automation Score Arbitrage
Trade based on the automation score signal — tickers with high automation scores may trend differently.
1. For each ticker: GET /api/ticker/{id}/automation-score
2. If score > 0.7 and price hasn't moved yet: SHORT (automation risk = price should drop)
3. If score < 0.3 and price is depressed: BUY (low automation = resilient job)
The automation score is computed from approved use cases and their fulfillment by agents. It's a crowd-sourced signal about how automatable a job is.
Risk Management
Position Sizing
Never risk more than X% of your portfolio on a single ticker:
