Strategy
Build trading strategy plans via the ChainGPT plugin: DCA (dollar-cost-averaging), grid trading, Hyperliquid funding-rate arbitrage, copy-trading from a target wallet, and DCA backtesting against historical price data. The strategy tools COMPUTE THE PLAN; Claude executes the steps one by one via Tier 1-3 tools (research, risk, dex, defi, hl, pm) — every action still gated by the mainnet acknowledgement refusal. Triggers: DCA, dollar cost average, grid trade, funding arb, copy trade, copy trading, strategy, backtest, replay strategy, recurring buy, periodic buy, ladder.From its SKILL.md
npx -y skills add ChainGPT-org/chaingpt-claude-skill --skill strategyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 15 stars15 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
4.0 KB, as published. Nobody here has run it
ChainGPT Strategy Skill
You build trading strategy plans — not execute them autonomously. Every plan output by these tools lists the exact Tier 1-3 tools Claude should call to execute the steps, in order. Each executing step still goes through the existing mainnet-ack gates. This keeps the agent layer reviewable and refusal-safe.
Tools
| Tool | What | Output |
|---|---|---|
chaingpt_strategy_dca_plan | DCA schedule for one token | List of buys (timestamp + USD size) + the exact MCP calls to execute each |
chaingpt_strategy_grid_plan | Grid-trading ladder | Buy + sell limit levels with sizes; HL / PM / DEX-flavored execution |
chaingpt_strategy_funding_arb_plan | HL funding-rate carry suggester | Side / leverage / hourly+daily carry estimate + execution sequence |
chaingpt_strategy_copy_plan | Mirror a target wallet's swaps | Step-by-step: fetch txs → decode → risk-check each outToken → scale + mirror |
chaingpt_backtest_dca | Replay DCA against CoinGecko history | DCA P&L vs buy-and-hold baseline |
Execution discipline
When the user asks "build me a DCA into ETH for $1000 over a week":
- Call
chaingpt_strategy_dca_plan outToken=<weth-on-base> network=base totalUsd=1000 intervals=7 cadenceHours=24 - Show the plan to the user. Surface the per-buy size, cadence, and pre-flight tools. Get confirmation.
- For each step in the plan, execute via
chaingpt_dex_build_swap_tx network=base ... acknowledgeMainnet=true. The user signs each one externally. - After execution, optionally call
chaingpt_onchain_tx hash=…to confirm fill.
Never auto-loop without the user's express turn-by-turn confirmation. If you need true automation, use a cron / scheduler tool — the strategy tools themselves don't fire orders.
Refusal protocol
These planner tools are read-only, so they don't refuse — but the executing tools they recommend (chaingpt_dex_build_swap_tx, chaingpt_hl_place_order_payload, chaingpt_pm_place_order_payload) all require acknowledgeMainnet: true. Surface that to the user before any execution.
What this skill does NOT do
- Custody. Plans are returned; user signs every step externally.
- Truly autonomous execution. No daemon, no cron loop, no auto-rebalancing. Each step is a separate user-signed transaction.
- ERC-4337 session keys with bounded auto-execution — deferred to a follow-up that needs its own security review.
- Strategy persistence. Plans aren't stored; each call recomputes. For long-running strategies, save the plan output to your own data layer.
- Live order-management. Cancellation + replacement is the user's responsibility; planner doesn't track open orders.
Backtesting caveats
chaingpt_backtest_dca uses CoinGecko's free /market_chart endpoint. Caveats:
- Max 90 days of daily candles on the free tier (some coins).
- Past performance doesn't predict future performance — DCA tends to win in choppy / down-then-up markets, B&H wins in steady uptrends.
- Backtest ignores gas costs, slippage, and the bid-ask spread — real-world DCA returns will be lower.
Credit accounting
Strategy planners + backtester cost 0 ChainGPT credits — the data comes from public APIs (Hyperliquid, CoinGecko). Credit funnel comes from the executed steps (each DEX swap triggers a chaingpt_risk_token pre-flight; each Aave borrow triggers a chaingpt_defi_aave_health read; each new-token research triggers chaingpt_intel_token which burns the news-fetch credit).