> ## Documentation Index > Fetch the complete documentation index at: https://docs.shekel.xyz/llms.txt > Use this file to discover all available pages before exploring further. # Data Sources > Every market signal your agent can see — and the exact field names you can reference in your strategy Each run, your agent receives a **live context package** before it makes a trading decision. You choose which sources go into that package under **Modify Agent → Data Sources**. The richer the context, the better the agent can reason — but premium sources cost extra credits, so turn on only what your strategy actually uses. 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 | The agent reads your strategy as English narrative — it does **not** parse field paths automatically. To make it reason about a specific value, **name the field and explain how to read it** in your prompt (e.g. *"stand down when `adx.4h` \< 20 — low-trend regime"*). The exact field names live in the [Data Points Reference](#data-points-reference) below. *** ## 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. Max pain is a **magnet, not a wall** — strong news can punch through it. Backtest coverage starts **2026-05-11** (when Shekel began indexing it); earlier backtest dates simply run without a max-pain section. Best for liquidity-event strategies and 4h+ swing trading; less useful for pure momentum. ## 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](/agent/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. *"when `rsi.4h` > 70 AND `funding_rate.annualized_pct` > 30%, treat as exhaustion."* Names match what the model literally sees inside the ``, ``, 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. **Symbol & price** ``` symbol "TICKER/USDC:USDC" base token symbol (e.g. "BTC") quote quote currency (always "USDC") mark_price current Hyperliquid mark price mid_price current Hyperliquid mid price price_change_24h absolute USD change over 24h price_change_percent_24h % change over 24h ``` **Funding rate** ``` funding_rate.current_value raw per-payment funding (decimal) funding_rate.annualized_pct annualized funding rate % ``` **Per-timeframe technicals** (`{tf}` = 15m, 1h, 4h, 1d) ``` rsi.{tf} Relative Strength Index (14) atr.{tf} Average True Range (14, raw price) atr.percentage_{tf} ATR as % of price (use for dynamic stops) macd_line_slope.{tf}.macd_line MACD line (12/26/9) macd_line_slope.{tf}.signal_line macd_line_slope.{tf}.histogram MACD histogram (line − signal) obv.{tf} On-Balance Volume (cumulative) adx.{tf} Avg Directional Index (14). <20 weak / >25 trending vwap.{tf} Volume-Weighted Avg Price (daily reset) ``` **Bollinger Bands** (1h, 20-period, 2σ) ``` bollinger.band_width (upper − lower) / middle — regime gauge bollinger.upper_band bollinger.lower_band bollinger.middle_band 20-period SMA (1h) ``` **EMA grid** — 4 timeframes (1h, 4h, daily, weekly) × 3 periods (20, 50, 200), distance + slope per cell: ``` ema.{tf}_{period}_distance_pct signed % from price to EMA (+ above, − below) ema.{tf}_{period}_slope_pct 5-bar % change of the EMA (+ up-sloping) ``` e.g. `ema.daily_20_distance_pct`, `ema.4h_50_slope_pct`. A daily `|slope_pct| < ~0.1%` is effectively flat. **StochRSI** (14/14/3) ``` stochrsi_4h [0,1]. <0.2 oversold, >0.8 overbought stochrsi_1d higher-TF confluence ``` **Swing structure** (Bill Williams fractal pivots, confirmed-only) ``` swing.4h.last_pivot_high.price / .bars_ago (4h micro: N=2) swing.4h.prev_pivot_high / last_pivot_low / prev_pivot_low swing.1d.last_pivot_high.price / .bars_ago (daily structural: N=5) swing.1d.prev_pivot_high / last_pivot_low / prev_pivot_low ``` Use for *"broke last swing low → exit long"* or *"higher highs + higher lows → uptrend intact."* Fresh pivots (`bars_ago` \< 10 on 4h, \< 5 on 1d) are higher-conviction. ``` asset token symbol open_interest.value current OI (notional) open_interest.change_pct_1h / _4h / _24h funding.current_value current funding (decimal) funding.annualized_pct funding.twap_1h_annualized funding.twap_8h_annualized funding.twap_24h_annualized mark_index_spread_bps perp premium/discount vs index (+ perp premium = longs paying; − = shorts paying) basis_bps basis spread vs reference max_leverage_available current HL max leverage cap _meta.indexer_observed_at when snapshot was taken _meta.indexer_latency_sec age of snapshot (seconds) _meta.stale_warning true if snapshot > 30 min old ``` ``` status "success" data.value 0–100 score data.value_classification "Extreme Fear" … "Extreme Greed" data.timestamp Unix seconds ``` Narrative text, not structured numbers. ``` recap paragraph(s) of recent macro + crypto headlines/themes generated_at timestamp of the recap ``` Reference in prompts like *"if news mentions a Fed pivot, increase risk aversion."* ``` long_short_ratio_4h global L/S ratio (1.0 balanced; >1 longs; <1 shorts) ls_signal "long_dominance" / "short_dominance" / "neutral" long_liq_dominance_4h % of 4h liquidations that were longs (>0.6 long capitulation; <0.4 short squeeze) liq_signal "long_capitulation" / "short_squeeze" / "neutral" oi_change_4h_pct open interest % change over 4h avg_funding_rate_oi OI-weighted average funding rate ts snapshot timestamp ``` Array of per-token records (BTC, ETH, SOL, XRP pinned; others rotate): ``` token symbol total_positions # of tracked whale positions position_percentage e.g. "60% SHORT" money_percentage notional-weighted bias (e.g. "86% SHORT") percentage numeric majority side % position "LONG" / "SHORT" / "NEUTRAL" signal_duration "short-term" / "mid-term" / "long-term" avg_hold_hours / median_hold_hours total_notional_value total $ of all tracked positions net_notional_value net directional exposure (signed) ``` Per symbol (BTC, ETH primarily), from Athena AI: ``` Symbol Monthend_MP / Quarterend_MP max-pain projections Top_Long / Top_Short % of top traders long/short Global_Long / Global_Short % of all traders long/short Top_Ratio / Global_Ratio long/short ratios Funding_Rate / Close_Basis Futures_Signal "Strong Long" … "Very Short" Options_Shortterm_Signal short-term options bias Options_Midterm_Signal ~1 month Options_Longterm_Signal ~3+ month Athena_Wisdom_Basis / _Shortterm / _Midterm / _Longterm composites ``` ``` symbol / current_price near_term.window "24h" near_term.long_max_pain.price / .liq_level_usd / .distance_pct near_term.short_max_pain.price / .liq_level_usd / .distance_pct extended.window "7d" extended.long_max_pain.* / extended.short_max_pain.* # top-level mirror (defaults to near-term) long_max_pain.* / short_max_pain.* max_pain_price single canonical max-pain price distance_pct % distance from current price ``` Both windows update every \~30 min across \~30 tokens. ``` asset "BTC" or "ETH" latest_date last observation (YYYY-MM-DD) latest_flow_usd most recent daily net flow ($, + inflow / − outflow) latest_price_usd spot price at last observation trend_7d_net 7-day cumulative net flow ($) trend_30d_net 30-day cumulative net flow ($) ts snapshot timestamp ``` ### 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: ```text theme={null} "Stand down when adx.4h < 20 — low-trend regime." "Only take longs when ema.daily_20_distance_pct > 0 AND ema.daily_20_slope_pct > 0 (price above an up-sloping 20-day EMA)." "Exit long if price closes below swing.1d.last_pivot_low.price — structural break against the position." "If liq_signal is 'long_capitulation' AND ema.4h_50_distance_pct < 0, treat as a bullish refuel candidate." ``` Whatever data sources you enable for **live** trading, the [Backtest Engine](/backtesting/overview) 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).