Free sources are on by default. Premium sources cost extra credits per ticker, per scheduled run — the credit chip next to each toggle shows the price (e.g.
+10cr/ticker). A run with four +10 premiums on a 5-token whitelist adds 4 × 10 × 5 = 200 credits over the base model cost. Pay only for what you enable.Sources at a glance
| Source | Cost / ticker / run | Default | What it gives the agent |
|---|---|---|---|
| Token Data | Free | ON | Price, technicals, EMAs, swing structure (Hyperliquid) |
| Fear & Greed Index | Free | ON | Macro sentiment regime (0–100) |
| Token Derivatives | +15 cr | OFF | Per-perp OI, funding TWAPs, basis, mark/index spread |
| Sentiment | +10 cr | ON | 12h macro + crypto news recap (Gloria AI) |
| Liquidation Data | +20 cr | ON | Long/short ratio, liquidation flow, OI change (CoinGlass) |
| Smart Money | +10 cr | ON | Whale positioning & notional flows by token |
| Futures/Options Signals | +10 cr | ON | Derivatives directional bias + confidence (Athena AI) |
| Max Pain | +50 cr | OFF | Institutional liquidation max-pain magnets (CoinGlass Pro) |
| ETF Flows | +5 cr | ON | Daily net BTC + ETH spot-ETF flows (BTC/ETH only) |
| Custom Endpoint | Free | — | Your own API → injected as a custom context block |
Token Data — Free · ON
Per-token live snapshot: price, order-book depth, funding rate, open interest, RSI, MACD, VWAP, Bollinger Bands, a full EMA grid, and swing structure — sourced directly from Hyperliquid. How to leverage: this is the bedrock context — leave it on unless you have a very good reason. Any strategy that mentions RSI, MACD, VWAP, EMA distance, or order-book imbalance relies on it. Without it your agent is flying blind on technicals.Fear & Greed Index — Free · ON
The crypto Fear & Greed Index (0 = extreme fear, 100 = extreme greed) — a macro regime indicator. How to leverage: best as a regime filter, not a trade trigger. Pairs powerfully with Smart Money — retail panicking (extreme fear) while whales accumulate is one of the highest-conviction contrarian setups on the platform.Token Derivatives — +15 cr · OFF
Per-ticker live perpetual structure from Hyperliquid via Shekel’s indexer: open interest with 1h/4h/24h change, funding rate plus 1h/8h/24h TWAPs, perp mark/index spread, basis, and max available leverage. How to leverage: for strategies that read positioning — funding TWAP divergences, OI building into a move, perp premium/discount as a crowding gauge. Heavier than Token Data’s basic funding field; turn it on when your edge is derivatives-structure-driven.Sentiment — +10 cr · ON
12-hour macro and crypto news recaps from Gloria AI — distilled headlines and themes the agent can read while deciding. How to leverage: for strategies that react to narrative (Fed pivots, ETF approvals, regulatory or exchange news). Purely technical prompts (“RSI cross + EMA confluence”) can leave it off and save the credits.Liquidation Data — +20 cr · ON
CoinGlass derivatives feed: long/short ratio, aggregated liquidation flow (longs vs shorts wiped out), OI percent-change, and OI-weighted funding. How to leverage: tells you what’s happening under the price — who’s getting hurt and how leveraged the market is. Needed for fade-the-flush, long-capitulation, short-squeeze, or funding-extreme strategies. Funding-rate divergence from price action is a classic tell.Smart Money — +10 cr · ON
Whale positioning and notional flows per token, from leaderboard-wallet activity — long/short bias by token from the largest positioned traders. How to leverage: for follow-smart-money or fade-retail-when-whales-disagree setups. Most powerful when whale positioning contradicts retail Fear & Greed. Whales are early but not always right — use as conviction weighting, not gospel.Futures/Options Signals — +10 cr · ON
Directional signals from futures and options markets with confidence scores (Athena AI): basis spreads, term-structure shifts, and short/medium/long-term bias from derivatives positioning. How to leverage: especially for swing strategies (1h+ intervals) where derivatives lead spot by hours-to-days. Short-frequency scalping (15m/30m) gets less — the signal cadence doesn’t match.Max Pain — +50 cr · OFF
Institutional-grade liquidation max-pain levels — the price zone where the most leveraged positions across the market would be wiped out. The same data MMs and prop desks use to map liquidity targets (CoinGlass Pro tier). How to leverage: max pain acts as a probability-weighted price magnet — markets statistically gravitate toward the level that maximizes liquidation damage on the overcrowded side. Use for “limit orders at liquidity targets,” “fade extended moves toward max pain,” or “size up when distance from max pain exceeds X%.” Combine with Liquidation Data: max pain tells you where the liquidity sits, liquidation flow tells you who is getting hurt right now.ETF Flows — +5 cr · ON (BTC + ETH only)
Daily net flow into BTC and ETH spot ETFs — an institutional macro-demand signal. Most recent daily flow plus 7-day and 30-day cumulative trends. How to leverage: a slow macro tide, not a trade trigger. Sustained inflows support a structurally bullish bias on BTC/ETH; persistent outflows are a caution flag.Custom Endpoint — Free · advanced
Feed your agent any external data — proprietary signals, on-chain metrics, custom indicators — by pointing it at your own API. See Custom Data for the full request/response contract (live and backtest).Data Points Reference
The complete field-by-field schema of everything your agent sees, per source. Use these exact names in your strategy prompt to make the agent reason about specific values — e.g. “whenrsi.4h > 70 AND funding_rate.annualized_pct > 30%, treat as exhaustion.” Names match what the model literally sees inside the <token_data>, <liquidation_data>, etc. blocks of the prompt. Unavailable values (e.g. a weekly 200-EMA on a young token) come through as null and the agent handles that gracefully.
Token Data (free, ON)
Token Data (free, ON)
Symbol & priceFunding ratePer-timeframe technicals (Bollinger Bands (1h, 20-period, 2σ)EMA grid — 4 timeframes (1h, 4h, daily, weekly) × 3 periods (20, 50, 200), distance + slope per cell:e.g. Swing structure (Bill Williams fractal pivots, confirmed-only)Use for “broke last swing low → exit long” or “higher highs + higher lows → uptrend intact.” Fresh pivots (
{tf} = 15m, 1h, 4h, 1d)ema.daily_20_distance_pct, ema.4h_50_slope_pct. A daily |slope_pct| < ~0.1% is effectively flat.StochRSI (14/14/3)bars_ago < 10 on 4h, < 5 on 1d) are higher-conviction.Token Derivatives (+15 cr, OFF)
Token Derivatives (+15 cr, OFF)
Fear & Greed Index (free, ON)
Fear & Greed Index (free, ON)
Sentiment (+10 cr, ON)
Sentiment (+10 cr, ON)
Narrative text, not structured numbers.Reference in prompts like “if news mentions a Fed pivot, increase risk aversion.”
Liquidation Data (+20 cr, ON)
Liquidation Data (+20 cr, ON)
Smart Money (+10 cr, ON)
Smart Money (+10 cr, ON)
Array of per-token records (BTC, ETH, SOL, XRP pinned; others rotate):
Futures/Options Signals (+10 cr, ON)
Futures/Options Signals (+10 cr, ON)
Per symbol (BTC, ETH primarily), from Athena AI:
Max Pain (+50 cr, OFF)
Max Pain (+50 cr, OFF)
ETF Flows (+5 cr, ON · BTC + ETH only)
ETF Flows (+5 cr, ON · BTC + ETH only)
Referencing fields in your prompt
The agent reads your strategy as narrative and reasons about it alongside the live data — it doesn’t blindly execute rules. Naming specific fields gives it concrete anchors, which produces tighter, more repeatable decisions than vague phrasing. Examples that work:Whatever data sources you enable for live trading, the Backtest Engine feeds the agent the same sources at each historical decision point — so a strategy that leans on a given signal behaves the same in simulation as in production (subject to each source’s historical coverage window).