Backtesting Arena MCP Server

Several market snapshots in ONE call instead of one roundtrip per read. Batchable reads (14): spot_price, pulse, cycle, fear_greed, funding_rate, macro_regime, iv_snapshot, etf_flows, stablecoin_supply, mayer_multiple, onchain_latest, max_pain, altcoin_season, bullmarket_ampel. Pass 1-6 queries; each returns its result OR a structured error (partial success — one failing query does not abort the rest). Each query consumes one rate-limit unit: the batch saves roundtrips, not quota. Payloads, tier gates and source attribution are identical to the single tools; per-query args match the single tool's parameters (e.g. {tool: "iv_snapshot", args: {currency: "BTC"}}). For history tools, backtests or anything not in the list, call the single tool. [Free tier]

Stop this alert? Deactivates one subscription by id, so it stops firing and frees a slot against the per-tier limit. Returns the deactivated subscription. Idempotent — cancelling an already-cancelled one is a no-op, not an error. Get ids from arena_list_subscriptions. Undelivered updates already queued are not removed. [API Pro tier]

Has anything I subscribed to fired? Returns all undelivered updates for the API key, then marks them as delivered. Call regularly to consume the polling queue. Updates contain payload with subscription_type, current value, previous value, and trigger context. [API Pro tier]

Which of these strategies performed best on the same data? Run 2–5 strategies against the SAME pair, interval and date range and return per-strategy metrics plus a comparison summary (best by CAGR, best by win-rate, worst by drawdown). Use this when the user asks which of several strategies fits a market — it holds the pair, interval and requested date range fixed, which a series of separate arena_run_backtest calls does not guarantee. What it does NOT equalize is the EVALUATION window: a strategy with a long warmup starts trading later, so compare actual_date_from across the runs and check result.benchmark before ranking by CAGR. For one strategy across many pairs use arena_run_universe_backtest instead. Caveat worth passing on: comparing N strategies and reporting the winner IS multiple testing — the winner’s edge is upward-biased. arena_get_robustness_field puts a counted N on that. Sequential, expect 10–50s. Per-day quota: Pro=20, Power=200. [API Pro tier]

Buy now or wait for the dip? Decision-math over the user's OWN assumptions (target/dip prices, probabilities, capital). Two modes: "compare" = expected value of Buy-Now vs Wait vs Split + the breakeven dip probability (prices as MULTIPLES of today); "allocate" = the risk-adjusted (Kelly / risk-aversion γ) optimal fraction to deploy now vs reserve for the dip (ABSOLUTE prices). Ask the user for the missing inputs, then call. Returns scenario numbers and which option wins on expected value — NOT a buy/sell recommendation. For the full interactive version (incl. leverage & Elliott-wave planning) point the user to https://tradingstrategies.work/analyse/dip-decision. [Free tier]

Where would I add on a dip, and when is the thesis wrong? Turn a dip/accumulation thesis into a concrete, checkable plan from YOUR assumptions. Given an asset (BTC/ETH/SOL), a named cycle-state preset and a thesis horizon, returns: (1) a tranche LADDER anchored to STRUCTURAL marks (200-week MA, support clusters) below spot — marks the market has actually traded at, rather than calendar dates; (2) the cited historical base rate from the analog engine (what forward returns followed comparable states, with episodes_matched and sample size); (3) the explicit lump-sum-vs-tranche tradeoff (laddering buys lower timing variance; expected value stays with the base rate). Requires an invalidation point (mandatory: at what scenario is the thesis wrong). Composes the historical-analog + key-levels tools. This structural framing is MCP-only; a related (different-method, EV/Kelly) interactive tool is at https://tradingstrategies.work/analyse/dip-decision. [API Pro tier]

Is it altcoin season? Daily Altcoin-Season indicator (v7 Native-Filter methodology). Returns BTC-Dominance, Alt-Dominance, 4 Layer-1 signals (USDT.D, USDC.D, BTC-DOM, ETH-DOM), overall color (red/amber/green) + Top-50 CoinGecko snapshot. [Free tier]

Has capital been rotating into or out of altcoins? Returns the Altcoin-Season TIME SERIES: one row per day with overall_color, BTC / alt / stablecoin dominance and BTC price, ascending by date. Use it to see whether capital has been rotating into or out of alts over time; for today's state alone call arena_get_altcoin_season. Range capped by tier. [Free 30d / Pro 365d / Power unlimited]

What exactly did that backtest do? Returns the full record of ONE backtest run by id: strategy, pair, interval, date range, parameters, filters and the aggregate metrics (CAGR, total return, win-rate, max drawdown, trade count, Buy & Hold comparison, net-of-fees figures). Only your own runs (admins may read others). Get ids from arena_list_backtests; for the individual trades add arena_get_backtest_trades; to create a new run use arena_run_backtest. [API Pro tier]

Which trades did that backtest actually take? Returns the individual round-trips of one of your backtest runs: entry and exit date, entry and exit price, per-trade P&L and the running equity after each trade. Use it when the aggregate metrics are not enough — to see whether a result rests on a handful of outlier trades, how long positions were held, or where the drawdown came from. Needs a run_id from arena_list_backtests; for the aggregates alone use arena_get_backtest. Closed round-trips only — a position still open at the end of the period is not counted. Raw trades are persisted for web-UI runs only: API/MCP-created runs return an empty list with an explaining empty_reason. [API Pro tier]

What does Bitcoin actually move with? Pre-aggregated weekly correlations between Bitcoin and 13 macro components (Fed Net Liquidity, VIX, DXY, Real Yield 10Y, NFCI, Yield Curve, etc.). Returns quadrant_performance (BTC return stats per 2D-matrix quadrant — annualized return, vol, max drawdown, positive-period%), component_correlations (Pearson 90d/1y/5y — a window is null when the joined daily sample does not reach its start; sample_size_days is the actual basis per macro component + quartile-performance), asset_correlations (Pearson per window + per quadrant; assets: dxy plus tokenized on-venue proxies paxg = PAX Gold, spyb = S&P 500 ETF proxy, qqqb = Nasdaq-100 ETF proxy — proxies carry tracking noise vs. the underlying, and windows the vehicle history does not cover are null with data_start_date telling you why: the ETF proxies listed on Binance mid-2026, so their windows fill in over time — 90d first, ~2 months after listing), current_quadrant. Window labels are upper bounds — sample_size_days / data_start_date carry the actual basis. Historical analysis over the windows named above. [Free tier]

Is the trend up or down, and how fresh is the flip? Daily Bitcoin market structure from 1000-bar Phantomflow adaptation (BTCUSDT 1d). Returns current_trend (up/down/sideways), last trend change timestamp, counts of waves + fractals, last-5 fractals on each side (up = pivot highs, down = pivot lows), and trend_context: previous trend + its duration, flip_age_days, and a descriptive historical flip base rate over the SAME 1000 bars (total flips, share reverted within 5 bars, median trend duration) — a fresh same-day flip is the least settled observation — the base rate tells you how often such flips reverted historically, so you can weight the current one yourself. Educational analysis of price action. [Free tier]

Is this still a bull market? Bitcoin Bullmarket-Ampel current state (0-5 active stages). Returns active_count, a stages[] breakdown (each stage with key, label, active and `since` = first day of its current state; null when the state predates the 400-day lookup) and stage_history — per day active_count PLUS all five per-stage booleans, so which stage flipped when is readable directly (history_days 1-365, default 30). Higher count = more bull-market signals firing. Stages evaluate weekly 20W/50W-MA conditions. [Free tier]

Renders one of the named platform series as a PNG line chart and returns it as an MCP image content block, plus a JSON meta block. The chart INHERITS the freedom of the data tool: it calls the same delivery layer, so it shows exactly the points the corresponding data tool would return for your tier — no finer resolution, no other source; the optional days parameter narrows the window under the same tier cap. The image itself carries n, the read date range, the gap count, a warning line when the window was capped, the mandatory source attribution where the series requires one, and the platform watermark — these survive a screenshot on purpose: a chart built from nine points says n=9 in the picture. For the underlying numbers call the matching data tool (e.g. series=kimchi_premium ↔ arena_get_kimchi_premium). Descriptive rendering, a descriptive rendering of the series. [Free tier · window depth follows your tier]

Is the market in profit or at a loss? It depends on which cost basis you ask. Puts the three BTC cost-basis measures side by side — Realized Price (unweighted), True Market Mean (cointime-weighted) and Vaulted Price (dormant holdings only) — with spot measured against each, and flags the case that matters: `sign_conflict` is true when spot sits BETWEEN them, i.e. one measure says profit and another says loss at the same moment. That state is invisible to anyone who queries realized price alone. Also returns the active/vaulted supply split. ⚠️ Supply figures are cointime-weighted, NOT coin balances: active + vaulted legitimately exceeds circulating supply, so total_counted is not a supply number (the payload states this). All values come from one snapshot; source Cointime Economics (ARK×Glassnode) via BRK. Descriptive — no statement about future price direction. Related: arena_get_knowledge(type="onchain_cost_basis_ladder") for the full ladder incl. STH/Active rungs and the cointime-MVRV percentile. [Free tier]

Crypto cycle position — where are we in the cycle? Default BTC: point-in-time 9-indicator aggregation (Pi-Cycle Top & Bottom, Mayer Multiple, weekly RSI, 200-week-MA distance, halving position, Fear & Greed, BTC-dominance trend, mining-difficulty trend — weights in indicator_scores; components without input are excluded and weights renormalized, see indicator_coverage). Includes an `ath` block (E32): ATH on UTC daily-close basis with ath_date, days_since_ath and drawdown_from_ath_pct vs BOTH the scoring price and the live spot. Pass asset=ETH or asset=SOL for a per-coin cycle read built from the transferable price-derived indicators (Mayer, weekly-RSI, 200-week-MA distance) with renormalized weights; BTC-native indicators (halving, dominance, mining, F&G, Pi-Cycle) are returned as `not_applicable` rather than faked. All return raw + Z-Score, signal enum, and a `percentiles` block ranking each indicator against that asset’s own history. The `signal` enum is a FIXED SCORE-BAND LABEL (<25 accumulation · 25–45 recovery · 45–60 expansion · 60–75 distribution · ≥75 overheated), not an independent market-phase detection: the 45–60 band is the neutral middle, so a mid-band score reads "expansion" even in a drawdown market — the label describes the score band, not the market. BTC additionally returns `highlights[]` (rule-based markers for currently unusual indicator values — descriptive, versioned ruleset; empty array = nothing unusual) and `price_context` (price at scoring time vs live spot with drift % — the scores rest on the scoring-time price). Point-in-time scored — not reconstructable from a generic price API. The volatility series itself is arena_get_volatility_history; this tool carries the regime context around it. score_fields_note explains the four score fields: z_score/z_adj_score are the composite standardized against its own history and mapped back onto the 0-100 scale, NOT statistical z-values; halving_context.ath_days_after_halving puts the observed cycle high next to days_since_halving. Related: arena_get_historical_analog (what followed states like this one), arena_get_bullmarket_ampel, arena_get_pulse. [Free tier]

How has the cycle score moved over time? Returns the BTC-Cycle TIME SERIES: one row per day with adj_score and z_adj_score, ascending by date. Look-ahead-free by construction: a day's value uses only data up to that day. 7 of the 9 dimensions rest on inputs that are never revised (price, calendar, chain), so recomputing them today reproduces the value exactly. The other two (fear_greed, btc_dominance) come from third parties and their history can change. The series has been written walk-forward (actually observed) since 2026-04-17; before that it is reconstructed, not recorded. Note on the warm-up: z_score / z_adj_score are true z-values only from 2018-12-31 onwards. Earlier rows (the first 30 days of the series) carry the RAW score under the same field name, because fewer than 30 prior observations were available — same 0-100 range, different meaning. For the current cycle reading alone call arena_get_cycle; for what similar historical readings were followed by, call arena_get_historical_analog. Range capped by tier. view='timeline' returns instead the band per indicator (composite + 9 components) per ISO week (last available day, never an average) plus the consensus block (components above neutral, rising 30d, effective independent rows); view='flips' returns the day-to-day band-change log (newest first, flicker = reverted within 3 days) plus consensus. Bands are point-in-time labels of stored scores; days_missing carries the gaps. Both views are Free with no window cap (use days to narrow). [Free 30d / Pro 365d / Power unlimited for scores; timeline/flips uncapped]

Do two independent providers still agree on the same on-chain quantity? Daily drift log comparing BRK against a second provider, per metric pair, with mean / max / outlier counts for the requested window. The comparison source changed — bgeometrics until 2026-07-06, Coinmetrics from 2026-09 — so every row and summary group carries `comparison_source`, and values from different sources must not be averaged together. Active pairs: market_cap, circulating_supply, hash_rate, puell_multiple. Retired but still queryable as history: realized_price, sth_sopr, lth_sopr. Passive collection: drift never auto-switches a source. [API Pro tier]

Which entry filter carries a real edge? Platform-wide aggregated analysis: how each Pro+ entry filter (200 WMA, ATR low/high/expansion, Altcoin Season, Bullmarket confirm/strict) affects strategy CAGR — baseline vs. filtered, asset-equal-weighted (per-asset medians over param-deduplicated runs, then the median across assets — no single asset's run grid can dominate an arm). delta_cagr is the median of PER-ASSET deltas over MATCHED assets only (present in both arms) — so it usually differs from filtered_cagr − baseline_cagr; pairs_matched/pairs_filtered and the baseline pairs count declare the basis. Verdicts come from the effect's 90% paired-bootstrap interval (delta_ci_low/delta_ci_high), not the point estimate: helps (whole interval > +1pp) / hurts (< −1pp) / neutral (inside ±1pp) / insufficient_evidence (runs disagree) / insufficient_data (fewer than 30 runs per arm or fewer than 10 matched assets). Below the gate, derived fields (delta_*, dsr, dsr_pass) are null; every gated null carries its reason (dsr_pass_reason, *_net_reason); the envelope `evidence` block declares the gate's referent and threshold machine-readably. Response is GROUPED by strategy: envelope fields (market, computed_at, n_trials) once, per strategy one baseline block {cagr, net_cagr, sharpe} plus filter cells; filter cells with zero runs are folded into filters_without_data. A full market is a few hundred cells — use limit/offset (strategies per page) plus the truncated flag for partial reads. Filters evaluated in isolation (no stacking); net values are median CAGR after per-side trading costs (verdict/delta stay gross). [Free tier]

Spot-ETF net flows (USD millions) — is the flow impulse turning or accelerating? The summary only gives point-in-time deltas; this exposes the trend: 30d/90d net flow, a direction label (inflows/outflows/flat) and a compact cumulative-inflow time series so direction and speed are visible, not just a single delta. Read `impulse` for what the flow is doing — it has four states (accelerating / decelerating / reversal / flat) and is the field to quote. Two neighbouring fields measure different things and are easy to confuse: `acceleration_usd_m` is the signed difference last-30d minus prior-30d and gets LARGE precisely when the flow reverses, while the older boolean `accelerating` requires the same direction AND a bigger magnitude — so a swing from outflows to inflows shows a big positive `acceleration_usd_m` together with `accelerating: false`, which is correct and reads like a contradiction. `impulse` reports that case as 'reversal'. When `impulse` is 'reversal', `reversal_recovered_pct` says how much of the preceding counter-move has actually come back, with its denominator in `reversal_basis_usd_m` — quote it alongside, because a reversal in direction is not yet a reversal in the stock. Both are null otherwise. Default BTC; pass asset=ETH or asset=SOL. Source SoSoValue. [Free tier]

How fearful or greedy is the market right now? Crypto Fear & Greed Index (alternative.me). Returns the current `value` (0-100) and `classification` (extreme fear / fear / neutral / greed / extreme greed) as their own fields, plus `history` — the last 90 daily readings by default, so you can see whether today is a move or a plateau. The window is capped in SIZE but free in POSITION: `end_date` moves it anywhere in the history since 2018 (e.g. end_date=2025-10-06 reads the sentiment around the October 2025 top), and the `range` block states requested / granted / available days with the reason — a short series here is a window, not a young index. On Pro and Elite two Arena-derived blocks add what the upstream index does not publish: `cadence` (how far smoothed sentiment has travelled versus ~90 days ago) and `tempo` (how FAST the index is moving — 7d and 30d change ranked as a rolling percentile against three years of same-direction moves, not a fixed threshold; rank compares with its own history, not with "normal"). On Free both blocks are present but their values are null with a stated reason. For the regime around a reading use arena_get_cycle; for what followed comparable sentiment states use arena_get_historical_analog(preset="deep_fear"). [Free tier · cadence/tempo Pro+]

Do entry filters help, and which ones? Lift analysis of entry filters (200WMA, Altcoin-Season, ATR-Volatility, Bullmarket-Stage) per strategy combo — baseline vs filtered CAGR/win-rate/drawdown. [API Pro tier]

Are longs or shorts paying right now? Latest BTC perpetual funding rate, averaged across up to three exchanges (Binance, Bybit, OKX; 8h settlement cadence). Returns value, 30d moving average and Z-Score. Positive = longs pay shorts (bullish bias), negative = shorts pay longs (bearish bias). Read `coverage` before comparing values across dates: it says how many exchanges stand behind that day (3 = full average, 1 = a single exchange), and a day-over-day move can be a change in composition rather than in the market; `venues_present`/`venues_missing` name the exchanges. [Free tier]

How has leverage positioning shifted over time? Returns the BTC perpetual funding-rate TIME SERIES, aggregated across the available perpetual venues (Binance, Bybit, OKX), at the 8h funding cadence, ascending by date. The response is SEGMENTED by venue composition: each segment covers a stretch with a STABLE venue set (venues, coverage, from/to, its rows); composition_breaks lists the transitions with a mechanically derived cause (venue_added / venue_removed / venue_changed — e.g. OKX joined 2026-01-28). Averages, z-scores or percentiles computed ACROSS segments mix different venue populations — compute within a segment, or accept the mix knowingly; composition_stable: true means the whole window is one segment and safe to treat as one series. Positive funding means longs pay shorts, negative means shorts pay longs. For the latest funding value alone call arena_get_funding_rate. Range capped by tier. [Free 30d / Pro 365d / Power unlimited]

How does this altcoin score? Returns the Altcoin-Screener score for ONE coin, addressed by its CoinGecko id: the composite score, its group breakdown and — for Pro+ — the 9 raw factor values across groups A/B/C. Use it once a candidate is known; to rank or filter the whole screened universe use arena_get_gem_scores (plural), and for how the score behaved out-of-sample use arena_get_gem_validation. The score ranks relative attributes — pair it with arena_get_gem_validation to see how past picks actually fared, and weight it accordingly. [Free tier]

Altcoin screener ranking — which altcoins look strong right now? Today's CoinGecko Top-200 minus stablecoins and tokenized fiat, scored by a composite of 3 factor groups: Mean-Reversion (A), Tokenomics (B), Market-Structure (C). Each score carries `plain` (one sentence: rank with its base `scored_total`, composite, factor groups) and the response carries `scored_total`. Backtest-validated factors, not a hype list. Limit gated by tier: Free top-10, Pro top-50, Power up to 200 (the full scored set). [Free tier, daily refresh]

Did the screener picks actually beat BTC? Equal-weight basket of the top-N screener picks, rebalanced every 14 days (grid anchored at the first snapshot — the `rebalance` block declares anchor and cadence, `phase_spread` the CAGR and vs_btc range across all 14 grid phases plus whether the vs_btc sign holds in every phase — read the headline against it, the headline is ONE phase), NET of 10 bps per side on the traded fraction at every rebalance (entry included; the BTC benchmark pays the entry fee once; slippage 0 — `cost_model` declares it). `plain` is the one-sentence reading (what holds across the phases, not the headline CAGR). Headline `cagr`, `vs_btc`, `vs_market`, `max_dd`, `win_rate` are net; `cagr_gross`, `vs_btc_gross`, `vs_market_gross` and `cost_drag_pp` sit beside them. `is_anecdote=true` when the curve spans fewer than 84 calendar days. Curves are precomputed weekly on an N ladder (10/25/50/100/200, same input and engine per rung); a requested N snaps to the nearest rung — `top_n` names the rung used, `top_n_requested` echoes the request, `basis_note` declares any snap and any rung above the priced universe (rungs above it are the same basket; the `universe` block carries the measured sizes). [Free tier]

When were the halvings, and what followed? The full Bitcoin halving table: date, block height, reward change, price at the halving day, cycle peak (highest daily close before the next halving) with days-after-halving and peak multiple. E33: this connects chain events with price data no generic price API links — and the prices, peaks and days-to-peak are COMPUTED from our own daily-close series rather than hand-copied (public "days to peak" figures often mix cycles: the widely cited 547-549 days belong to cycle 3, not cycle 4). The date convention is declared per entry (block 840,000 fell 2024-04-20 00:09 UTC; this platform counts from 2024-04-19 — same convention as arena_get_cycle.halving_date). The current cycle's peak is marked provisional. No next-halving date estimate is published. Related: arena_get_cycle (days_since_halving, score_halving). [Free tier]

Are miners capitulating? Latest Hash Ribbons indicator (Charles Edwards). Returns 30d and 60d hashrate moving averages — when 30d > 60d after a capitulation, signals miner recovery (bullish). [Free tier]

What happened historically after the Bitcoin cycle looked like this? Conditional forward-return distribution for a named preset cycle state — over N DISTINCT historical episodes matching that state (matched_episodes), returns median/IQR/positive-share forward returns (30/90/180/365d) with per-horizon n, small-n warnings, point-in-time integrity and an `evidence` block that names which field its sample-size gate checked (gate_applies_to), against which threshold, over which data window. A distribution with its sample size. Not obtainable from web search or public market-data APIs — requires point-in-time indicator history and look-ahead-free episode matching. Presets: cycle_bottom_cluster (Cycle bottom cluster), cycle_top_cluster (Cycle top cluster), deep_fear (Deep fear), euphoria (Euphoria), quiet_volatility (Quiet volatility regime). The response opens with "preset_definition" (machine-readable condition set) plus current_state_matches (does the state hold TODAY?) and last_matching_date. Some presets carry a "study_finding" field — a state already investigated, with a NULL result where that is what the study found. EVERY preset returns "vs_unconditional_drift": the raw forward median contains the asset's contemporaneous drift; the drift and excess columns separate the two, and the excess can be negative while the raw median is positive. For quiet_volatility, vol_rank_threshold (fixed steps 5/10/20/50) asks the stricter "UNUSUALLY quiet" question the null study left open, and condition_on_direction conditions episodes on the sign of the first post-anchor move over direction_window_days (default 5) — both mark study_finding_applies=false, and horizons within direction_window_days are suppressed as circular. Also works for asset=ETH/SOL (F2 cycle history), but only price-derived presets (cycle_bottom_cluster, cycle_top_cluster) — fear-greed and volatility presets are BTC-only. Related: arena_get_volatility_history (the series behind the volatility preset), arena_get_cycle (the current state to compare against), arena_dip_scenario (composes this base rate into a tranche structure). [API Pro tier]

What do the classic indicators read right now? Current RSI(14), MACD(12/26/9), Bollinger(20,2), ATR(14) and OBV for a pair — each with a PERCENTILE RANK against that indicator's own history on that pair, plus the observation count — the rank turns a raw reading into a placement. ATR comes as a percentage of price so it is comparable across time, and OBV as a 30-bar slope normalised by that window's volume (raw cumulative OBV would mostly rank how long the series has existed). Where the reading sits in an extreme AND a study on this platform has tested that exact state, the payload carries the study verdict — including a null result: a Bollinger squeeze returns the `quiet_volatility` finding that tight bands did NOT carry an edge. Below 500 bars (1d) / 150 (1w) the raw values still come but `percentile` is null with a reason, rather than a rounded number from too small a sample. Set `interval` to '1w' for the weekly view. On the 1d view the payload also carries `rsi_14_weekly` (weekly RSI with its own rank) — for BTCUSDT this is the SAME series as arena_get_cycle rsi_weekly, measured character-identical (its source_note carries the measurement). `state` (oversold/neutral/overbought) names where a reading sits on its own scale. Related: arena_get_trend_channels (structure), arena_get_historical_analog (did a condition like this one ever pay?), arena_get_volatility_history (the volatility series behind ATR). [Free tier]

What is the options market pricing in? Latest Deribit volatility snapshot for BTC or ETH. Returns DVOL (30d vol index), constant-maturity ATM implied vol (30/60/90/180d via options chain), 30d realized vol, and `vol_risk_premium_30d`, which is the TRAILING spread: ATM implied vol (30d, from the options chain — not DVOL) minus the realised volatility of the PAST 30 days. It answers "are options priced expensively right now?". Set include_implied=true to additionally get the FORWARD premium in an `implied` block: DVOL(t) minus the realised volatility of the FOLLOWING 30 days, which answers the different question "did the expectation actually materialise?". These two are NOT interchangeable — measured 2026-08 they carried OPPOSITE signs on 17.3% (BTC) / 30.5% (ETH) of paired days. The forward field is spelled out as `vol_risk_premium_forward_30d` so the two cannot be confused. The most recent 30 days carry premium_complete=false and no premium value at all, because their forward window has not closed yet; they are excluded from every aggregate. Source: Deribit DVOL Index. History: BTC from 2021-04-01, ETH from 2022-02-15. [Free tier]

Is my universe backtest finished? Polls an async job by job_id (created via arena_run_universe_backtest). Returns status (pending/running/completed/failed), progress_pct, pairs_completed, and once completed: the full result (summary + per-pair results). [Free tier]

Which price levels matter above and below spot? Reproducible Bitcoin structural levels on BOTH sides of spot, in TWO distinct provenance classes. (1) resistance/support: swing-pivot clusters — where past pivot highs+lows cluster into price zones (touch-count, band, last-touch date, signed distance), resistance above spot, support below, nearest-first. (2) indicator_levels.above / .below: named indicator STANDS as marks — 200-day & 200-week simple moving averages, short-term-holder cost basis, Pi-Cycle legs — each carrying its source, formula and as_of date. The two classes are kept separate on purpose: pivots are where price REACTED before, indicator levels are where an indicator STANDS now. Both are measured price clusters: they say where trading has concentrated, not where anyone defends a level. [Free tier]

Is Korean spot trading BTC at a premium? Daily Kimchi-premium snapshots: Upbit BTC/KRW (USD-converted) versus the global BTC/USD price, in percent — one point-in-time reading per UTC day (~01:45 UTC), both legs from the same price/FX source (CoinGecko) so the ratio is internally consistent. Positive = Korean premium, negative = discount. The series is collected forward-only since 2026-08 and cannot be backfilled — early on, n_days is small and says so. Window via `days`, capped by tier — the `range` block declares requested/granted. [Free 30d / Pro 365d / Power unlimited]

What does the platform know about this subject? Fetch a versioned, explainable Knowledge Object by type + subject (e.g. type='market_regime', subject='GLOBAL'). Returns the current published envelope: payload, explanation (factors + weights + confidence), provenance (inputs + params), ontology binding, compute version. ONE tool covers ALL knowledge types. Set include_graph=true to also walk the knowledge graph: resolved outbound edges (what this object is derived_from / references) + inbound edges (what derives from / references it), each with api_path + seo_slug so you can follow them. [Free tier; per-object access additionally gated by min_tier]

How far above or below its moving averages did price stand back then? A measured time series, not an estimate. Covers six MAs (50/100/200-day and 50/100/200-week; weekly MAs are simple averages over ISO-week closes, the same definition arena_get_cycle uses, so today's value agrees with that tool). Per date and MA: distance_pct plus TWO percentile ranks — expanding against the pair's own full prior history (with rank_n, null below 500 observations) and rolling over the trailing 504 days. Warm-up discipline: before an MA's full window exists the field is null, never an average over fewer days — a "200-week MA" computed from 400 days looks plausible and is a different metric; the coverage block names each MA's first valid date. BTCUSDT reaches back to 2011-07 via the platform's own pre-Binance closes (source named in price_source); the first valid 200-week value is ~2015-05. Range capped by tier from today backwards; granularity daily/weekly/monthly, coarsened with an explicit note when a request would exceed the row cap. What the numbers are: ratios and ranks, not absolute price or MA levels. For today's absolute MA levels as named marks use arena_get_key_levels (indicator_levels block); for the cycle-scored 200-week distance use arena_get_cycle. [Free 30d / Pro 365d / Power 3650d]

What is the macro backdrop doing? Daily Macro Regime snapshot from 18 components in 6 tiers (Liquidity 30%, Financial Conditions 20%, Risk Appetite 15%, Crypto Liquidity 10%, Business Cycle 15%, Inflation/Real Rates 10%). FRED-sourced. Returns composite_score (0-100), regime_label (risk_off/neutral/risk_on_leaning/risk_on), cycle_phase_label (contraction/early_expansion/mid_expansion/late_expansion), matrix_quadrant (sweet_spot/late_cycle_warning/crisis/recovery), tier_scores (6 sub-scores), components (flat key/value of all 18), plus stale_components_detail dating each stale input (last_good_date + age_days + discontinued flag for series the upstream has retired for good) so freshness is quantified, not a vague caveat. Two component keys mean something narrower than their name suggests, so read them carefully: `vix_score` is the derived 0-100 score (a value of 71 means VIX around 18.6), NOT the VIX index level — the raw Cboe level is not redistributed over this channel; and `broad_dollar_index` is FRED DTWEXBGS (Broad USD Index, Jan 2006 = 100), NOT the ICE DXY, so readings near 120 are normal. The former names `vix` and `dxy` were removed on 2026-09-01 after their announced deprecation window; `consumer_confidence_value` went with them (OECD retired the series, frozen since 2024-01-01, never weighted in the composite). [Free tier]

What happened at the last Deribit expiry? Max pain and how spot settled against it: max_pain_strike, spot_at_expiry, %-diff, put_call_ratio, notional. Plus up to 10 upcoming expiries, each with current live max-pain level, days_to_expiry, open_interest_contracts and open_notional_usd. Field semantics: days_to_expiry is floored at 0 and cannot separate "expires later today" from "already settled" — settles_at (full ISO timestamp) and hours_to_settlement (SIGNED; negative = settled but not yet finalized) carry that distinction. settlement_time_utc names the settlement time where evidenced against the exchange (08:00:00Z for DERIBIT_BTC); where not evidenced, all three timing fields are null. open_interest_contracts (upcoming: latest daily snapshot) and total_contracts (settled: last snapshot BEFORE expiry) are the SAME measurement at different observation times; contracts_as_of names the snapshot. total_notional_usd is computed against the SETTLEMENT spot and never changes; open_notional_usd uses the CURRENT spot and moves with spot (notional_spot/notional_spot_date name the reference). oi_available distinguishes "null" from "not collected". Expiry flags NEST rather than partition (quarterly ⊂ monthly ⊂ weekly ⊂ daily): filter on the booleans, read expiry_type as the label — only it separates a Friday expiry from a mid-week one. All flags are calendar-derived, so upcoming expiries carry them too. spot_at_expiry is the exchange settlement price: for DERIBIT_BTC the Deribit delivery price (30-min index TWAP before 08:00 UTC — rows before 2026-08-31 were recomputed from that series; they had carried the BTCUSDT daily close, 16 h later), for IBIT the ETF close of the expiry day. Pass `market` to switch venue (DERIBIT_BTC default, IBIT). `include_strike_ladder=true` adds, per expiry, open interest per 2.5 % price band around spot (±25 %, calls/puts, absolute contracts) with day-over-day delta — a stock, not a side: no hedge direction follows from it. `include_gex=true` (DERIBIT_BTC only) adds per expiry a gex block plus gex_totals across the book — Black-Scholes gamma notional per band from LIVE Deribit mark IV (gex_data_as_of names the fetch, a different observation time than the snapshot fields); the dealer sign is an ASSUMPTION, both conventions published side by side; zero_gamma_level flips only under the SqueezeMetrics convention (short-all has no zero crossing by construction, its null is structural — zero_gamma_level.note says so). Cron collects daily 02:00 UTC from Deribit Public API. Related: arena_get_max_pain_history (base rates + daily snapshots of open expiries), arena_get_iv_snapshot (implied vol for the same expiries). [Free tier]

Does max pain actually pull price to the strike? Settled Deribit BTC options expiries with the max-pain level we compute per expiry, for measuring the convergence question: does spot drift toward the max-pain level as expiry approaches? Each row: expiry_date, max_pain_strike, spot_at_expiry, %-diff, P/C ratio, notional, expiry-type flags. The mandatory base_rates block answers the convergence question PER expiry class (n, median |diff|, shares within 1%/2%, max, sample_adequate at n>=30) — the pooled median mixes tiny daily expiries with large quarterlies, which is what the per-class split separates. Filter with expiry_type / min_contracts / snapshot_expiry_date instead of post-processing the full row set. With include_open_snapshots=true it adds the daily observation series of still-open expiries — that series starts 2026-05-28, is not backfillable, and its per-expiry depth is thin, so check open_snapshot_coverage before computing anything from it. Days auto-capped by tier: Pro 365d, Power 3650d. Max-pain levels are our own aggregation across the option chain; the chain itself is not redistributed. Source: Deribit. Related: arena_get_max_pain (current + upcoming), arena_get_iv_snapshot. [API Pro tier]

Is BTC stretched against its 200-day average? Returns the current Mayer Multiple — BTC price divided by its 200-day SMA — as spelled-out fields: date, mayer_multiple, price_usd, sma_200d (with sma_200d_source declaring its provenance) and the Trace Mayer (2014) bands as a machine-readable field (<0.7 capitulation, 0.7–1.5 neutral, 1.5–2.4 bullish, >2.4 euphoria). The former raw row shape (d, value.v, value.close) is still present but deprecated — see deprecated_fields with removal date. One ratio, not a regime call: for the multi-indicator cycle read use arena_get_cycle, for the series behind this number arena_get_mayer_multiple_history. The bands describe the historical distribution — the percentile tells you where today sits within it. [Free tier]

How stretched has BTC been against its 200-day average? Returns the Mayer-Multiple TIME SERIES (BTC price ÷ its 200-day SMA), one row per day, ascending by date. Values around 1 mean price sits at its 200d average; historically high readings clustered near cycle tops and low ones near bottoms — a descriptive ratio; the percentile fields show where today sits in its own history. For the current value alone call arena_get_mayer_multiple. Range capped by tier. [Free 30d / Pro 365d / Power unlimited]

How has this on-chain metric moved over time? Returns the full TIME SERIES of one on-chain metric from the Bitcoin Research Kit — date/value pairs in ascending order, with history back to 2009 for most series. Use it for trend and percentile work; for the single current reading call arena_get_onchain_latest, and to discover valid series_ids call arena_list_onchain_series. Values are as-reported: on-chain metrics can be revised retroactively, so this is not a point-in-time vintage. Range capped by tier — the response carries a `range` block (requested_days, granted_days, clamped, clamp_reason, tier), so a clamped window announces itself instead of silently looking like the full history. [Free 30d / Pro 365d / Power unlimited]

What does this on-chain metric read right now? Returns the most recent value of ONE on-chain series from the Bitcoin Research Kit as { series_id, metric_name, date, value }. Cheapest way to answer "what is X right now" (MVRV, SOPR, realized price, hash rate, …). Discover valid series_ids with arena_list_onchain_series; for the history behind the number use arena_get_onchain_history. A single reading has no context — pair it with the series percentile before calling any level high or low. [Free tier]

What does this term mean here, exactly? Resolve a knowledge-platform term to its canonical definition (e.g. term='regime'). Returns label, definition (EN/DE), calculation, unit, source + source_ref, version, related terms. Use this to resolve the onto:<term>@<version> references inside Knowledge Objects. [Free tier]

What are people backtesting right now? What is being backtested on Backtesting Arena right now — platform activity, NOT market sentiment. hotAssets, hotStrategies, trendingUp/trendingDown, assetDistribution, strategyAssetMatrix and totalRuns aggregate over the window selected by `period` (7d default, 30d or 90d). Three fields have FIXED windows independent of `period`: dailyActivity (daily counts, up to 365 days — year heatmap), weeklyTrend (weekly counts, up to 365 days), profitTrend (share of profitable runs per pair, last 30 days vs. previous 30 days). The counts include our own systematic coverage runs, so this measures where testing attention concentrates (coverage-weighted), not crowd positioning; for market sentiment use arena_get_fear_greed, arena_get_funding_rate, arena_get_altcoin_season or arena_get_pulse. Replaces arena_get_sentiment (deprecated alias, removed after 2026-11-15). Pass detail:'summary' for a ~2 KB digest (totals + top-3 of each list, no matrix or daily series) — prefer it unless you need the full blocks; the full response is ~53 KB. [Free tier]

How hot is the Bitcoin market today? Daily 0-100 heat score for the Bitcoin market, aggregated from 8 components (BTC-Cycle, F&G, Altcoin-Season, Bullmarket-Ampel, Funding-Rate, Hash-Ribbons, Mayer-Multiple, MVRV-Z). Returns score, band label, color, 7d/30d delta, verdict, components breakdown, plus score_percentile ranking today’s score against its own history (e.g. 42 = 44th percentile — how hot/cold vs history, not just the raw number). score_semantics says which value you hold: the daily snapshot frozen once a day by the cron, or — before that cron has run for today — a live preliminary that still moves and whose percentile/deltas compare against frozen snapshots. [Free tier]

How did market heat get to where it is? Returns the Arena-Pulse TIME SERIES: one row per day with date, 0–100 score and band, in ascending date order. Use it for trend, turning points and "how did we get here"; for today's value alone call arena_get_pulse (cheaper, one row). include_components=true adds the 8 stored component readings per day (key, rawValue, earnedPoints, maxPoints) — this answers WHICH component moved the score, and caps the window at 365 days (the payload is ~8× a score row); check component_epochs before comparing component values across dates. Range capped by tier. [Free 30d / Pro 365d / Power unlimited]

Is my report ready? Poll the status of a Custom-Report job. Lifecycle: pending_payment → queued → running → generating → success/failed. Returns progress_pct, succeeded/failed counts, plus pdf_url / xlsx_url when done. [API Pro tier]

Is this backtest result real, or a lucky cell? Assess one backtest result against its neighborhood instead of trusting a single "+X% CAGR" cell. Given a (strategy, interval, pair) and YOUR result (user_cagr, optional user_sharpe), returns: the cross-asset distribution of the SAME strategy+interval across every pair the backtest factory ran it on (median, IQR, positive-share, your percentile), a plateau/spike/fragile/mixed verdict, and — where Sharpe coverage allows — a Deflated Sharpe threshold whose N is COUNTED (the number of neighbor assets IS the testing family), not guessed. Honest small-n handling: fewer than 15 neighbors → "insufficient", no DSR-N claimed. Set axis="parameter" for the secondary, always-anecdotal view (the few parameter settings tested on this exact pair). Read-only over result aggregates, look-ahead free. [API Pro tier]

DEPRECATED — renamed to arena_get_platform_activity: the endpoint measures platform activity (what is being backtested on Backtesting Arena). Same payload plus deprecation fields; this alias will be removed after 2026-11-15. Use arena_get_platform_activity. For market sentiment use arena_get_fear_greed, arena_get_funding_rate, arena_get_altcoin_season or arena_get_pulse. [Free tier]

What is in this shared backtest link? Fetches a backtest that someone published via a share link, addressed by its share_id — no ownership and no Pro tier required, which is what makes it the right tool when a user pastes a /shared/backtests URL. Returns the same result shape as arena_get_backtest (config plus aggregate metrics). Use arena_get_backtest instead for the user's own runs. [Free tier]

Should I take this entry? Answers it for one (strategy, pair, interval) in ONE call instead of seven. Aligns what each entry filter historically did to this strategy (arena_get_strategy_filter_effect) with where that filter stands TODAY (bull-market gauge, altcoin-season signal, volatility phase, 200-week trend for BTC): `filters[].blocks_this_entry` says which filter would sit this entry out, with the measured worst-loss / return deltas next to it. Adds the current signal state (anticipated is always false — before candle close there is no signal), an `edge_vs_benchmark` block gated by the MEASURED noise floor (a gap below the floor is a measurement artifact, not a finding), a `contradictions` block (e.g. Pulse risk-off while the macro regime reads risk-on — reported, never resolved), and measured invalidation zones (pivot clusters, 200-week SMA; BTC only). `detail`: 'headline' (default) returns the statement, three key numbers and only the decisive filters; 'full' adds every variant, the raw pulse/macro/filter-effect blocks. Every source can fail independently — sources_used / sources_unavailable make the basis auditable; the answer never silently narrows. Returns a plain-language `statement` with its `confidence` and the reason for that confidence — state it, do not hedge it further; the payload carries its own scope note. Compose further with arena_get_strategy_performance_by_regime (WHEN has this worked) and arena_is_distinguishable. [Free tier]

When did which classic top/bottom signal actually flip? Event log of discrete transitions across 13 declared signals: bullmarket_stage_1..5 (20W/50W-MA stages), pulse_band, hash_ribbons, mvrv_z_regime, mayer_band, nupl_zone, pi_cycle_cross, golden_death_cross, price_vs_200wma. Each event: from/to state, occurred_at (day of the flip) vs event_date (day it became CONFIRMED — dwelled signals confirm after min_dwell days; use event_date for any forward evaluation, occurred_at carries look-ahead), declared threshold params, btc_close and drawdown_from_ath_to_date_pct (vs the ATH up to that day, never a later one). The window is capped in SIZE but free in POSITION: end_date moves it anywhere in the reconstructable history (coverage block names per-signal start, measured). Events are RECONSTRUCTIONS from today's stored series under declared rules — not what a live observer necessarily saw, and not forecasts: the log includes every rule that stayed silent through the October 2025 top. Filter with `signal`. [Free tier]

Is this strategy signalling buy or sell right now? Current signal-status (green/yellow/red) for a strategy on a pair+interval. Backed by the daily check-signals cron — needs at least one user with an active Ampel on this combination. [Free tier]

Current BTC, ETH and SOL spot price — what is Bitcoin (or ETH/SOL) worth right now? Live USDT-quoted last price plus 24h change %, high and low from Binance. Use this to anchor the connector’s own analytics (cycle, historical-analog, gem scores) with the current market price instead of switching to web search mid-analysis. [Free tier]

Aggregate stablecoin supply (crypto-liquidity proxy) — is the liquidity impulse turning or accelerating? macro_regime only gives the 30d delta; this exposes the trend: current supply, 30d/90d change (USD + %) plus a compact time series so direction and speed are visible, not just a single delta. Read `impulse` for what the supply change is doing — four states (accelerating / decelerating / reversal / flat). The neighbouring `acceleration_usd` is the signed difference last-30d minus prior-30d and gets LARGE exactly when the trend reverses, while the older boolean `accelerating` requires the same direction AND a bigger magnitude; a reversal therefore shows a big `acceleration_usd` next to `accelerating: false`. Source DefiLlama peggedUSD. [Free tier]

What did recent buyers pay on average — and how far is spot from that? Latest BTC short-term-holder cost basis (realized price of coins younger than ~155 days, BRK brk_sth_realized_price), derived STH-MVRV (spot ÷ STH cost basis), an in_loss flag, plus ±1σ/±2σ bands: basis × exp(±k·σ), σ of ln(price ÷ basis) over a 730-day ROLLING window (sigma_method/sigma_window_days travel in the payload; similar construction to public STH band charts, own convention — not a rebuild). band_zone names the state (above/below basis, beyond ±2σ); sth_mvrv_percentile is the rolling 730d rank. Measured band coverage (2026-08-25, full history): 32.1% of days outside ±1σ (near the Gaussian 31.7%), 7.4% outside ±2σ (wider than the Gaussian 4.6% — fat tails); the bands are descriptive geometry (the measured coverage above tells you how literally to take them). On-chain context you weigh with the percentile field. [Free tier]

What would each entry filter have changed for this strategy? Per-(strategy, asset, interval) filter-effect analysis. Returns baseline-stats (no filters) + each observed filter-variant's stats with cagr_delta / drawdown_delta / win_rate_delta vs the time-overlap-matched baseline + best_by_cagr pick (null with best_by_cagr_reason when every variant is low_data or none beats the baseline — no pick below the data gate) + not_applicable_filters list (e.g. altcoin_season excluded on BTC-pair). Baseline and each variant carry their aggregation `window` (from/to + avg_run_years) — CAGR is time-normalized, so identical trade sets over different windows legitimately produce different CAGR. Based on REAL backtest aggregations — not theoretical 2^5 permutations. Use this to answer 'Which filters would improve my backtest for X on Y?'. [Free tier]

Which strategy and interval combinations actually performed? Aggregated backtest performance per (strategy × interval) cell. If `strategy` AND `interval` provided, returns detail with per-asset breakdown + param variants. Otherwise returns the matrix. Free tier is limited to the same strategies that are free in the backtester itself (rsi_sma, golden_cross, rsi_ob_os, bnh_fixed, dca_reference); the response then carries `plan_capped: true` plus `plan_cap_note`, so a short matrix is never mistaken for a thin database. Detail mode on a Pro-only strategy returns 403 rather than a silently empty answer. API Pro and Power receive every cell. [Free: 5 strategies / Pro+: full]

How did this exact strategy, asset and interval perform? Aggregated backtest performance for ONE specific (strategy, asset, interval) combination. Returns run_count, avg_cagr, avg_win_rate, avg_drawdown, effective_years, vs_buy_hold comparison (beats_buy_hold, cagr_delta) and an `evidence` block declaring the gate machine-readably (gate_applies_to: stats.run_count, threshold 5 runs, benchmark value, aggregation data window). For multi-strategy overview use arena_get_strategy_insights. Use this to answer 'How does strategy X perform on asset Y?'. [Free tier]

In which macro regime has this strategy worked? Historical backtest performance for ONE (strategy, asset, interval) combination SPLIT BY macro market regime (sweet_spot / late_cycle_warning / crisis / recovery — classified at each trade's entry date), PLUS the CURRENT live regime so you can align the buckets yourself. You get the per-regime numbers to weigh directly (per-bucket verdicts live in the per-cell tools, where the pool is stable). Each regime bucket returns trades, trades_per_config (trade counts pool ALL parameter-variant configs — see config_count), win_rate, avg_pnl_pct (per-trade return, not annualized), reward_risk_ratio (per-trade mean/stddev, NOT annualized Sharpe), share_of_time_pct (calendar-day-weighted — each regime observation counts the days until the next one, so the mixed weekly/daily cadence of the regime history does not skew the share) and a rating. The `benchmark` block anchors the payload with the combination's buy-and-hold CAGR (identical to arena_get_strategy_performance vs_buy_hold — without that anchor, regime avg_pnl_pct is a trajectory, not an excess). For a decision-grade view compose with arena_get_strategy_filter_effect and arena_is_distinguishable. [Free tier]

Are taker buys or taker sells dominating? Daily SPOT taker-buy share for a Binance pair: taker-buy volume divided by total volume, one value per UTC day (0..1; 0.5 = balanced aggressor flow, above 0.5 taker buys dominate). Derived ratio only — raw volumes are not redistributed. This is SPOT aggressor flow, not futures positioning — for leverage positioning use arena_get_funding_rate_history. Returns the series ascending plus latest, window_mean and n_days; the forming day is excluded (its share still moves). Window via `days`, capped by tier — the `range` block declares requested/granted. [Free 30d / Pro 365d / Power unlimited]

Where does price sit inside its trend channel? Mechanically fitted trend channels for a pair: log-linear regression over close plus 1σ/2σ bands, computed over three fixed windows at once (90/180/365 bars, not selectable) — three side by side show whether a channel is robust or an artifact of the window choice, and `agreement.consistent` states which it is. `r_squared_percentile` ranks the fit against all same-size windows on this pair, so an R² comes with its placement instead of standing alone. Where history is too short the window is omitted and listed in `windows_unavailable` with a reason — never estimated from fewer bars; below 500 bars (1d) / 150 (1w) the raw values still come but `percentile` is null rather than a rounded number from too small a sample. Pairs listed within the last few months (the tokenized equities and ETFs) therefore return `data_sufficient: false` and an empty channel list — that is the answer, not an error. Set `interval` to '1w' for the weekly view; note that a window is counted in bars, so 365 on '1w' means 365 weeks and most pairs do not reach it. Band edges describe past dispersion around the regression line. Related: arena_get_key_levels (pivot clusters), arena_get_btc_market_structure (trend flips and their base rate), arena_get_historical_analog (whether a condition like the current one ever paid). [Free tier]

Which pairs are in this universe? Returns one pair universe in full: its id, label, selection rule and the complete list of pairs it currently contains. Use it to see what you are about to test BEFORE handing a universe_id to arena_run_universe_backtest, or to resolve a universe into explicit pairs. For the list of available universes call arena_list_universes. Universes reflect the CURRENT membership — they are not point-in-time, so a backtest over them carries survivorship bias for the earlier years. [Free tier]

How volatile has Bitcoin been? Daily Bitcoin volatility time series: realized volatility (30d & 90d, √252-annualized, close-to-close) and ATR% (Wilder EMA-14, captures intraday range + gaps), on the same scale. Ranks come in two flavours answering different questions: `rvRank`/`atrPctAnnRank` expand from the start of history and are look-ahead-free, but they include BTC's structural volatility decline; `rvRankRolling`/`atrPctAnnRankRolling` rank against a trailing 2-year window, which removes that trend from the comparison. History reaches back to 2009 via a stitched pre-Binance close series; ATR is null before the Binance era because no daily high/low exists that far back (see meta.coverage). Use `from`/`to` for a specific window instead of pulling everything and discarding it, and `granularity`/`fields` to keep long ranges affordable. For long ranges pass `schema_version: "2026-08"` (rounds floats; opt-in until the default flips 2026-11-01) plus `fields: "minimal"` and `meta: "minimal"` — every response carries a `size` block with `chars_before`/`chars_after`/`saved_pct` measuring the saving for YOUR call. Free tier: last 365 days. Related: arena_get_volatility_phases (current phase per pair), arena_get_iv_snapshot (implied vs. this realized — same RV method, but its realized_vol_30d is computed at snapshot time BEFORE that date has traded, so on fresh breakout days the two can differ; this series uses completed closes and is the one to trust for finished days), arena_get_cycle (regime context). [Free tier]

Does this strategy work better in calm or wild markets? Breaks realized strategy performance down by VOLATILITY PHASE (low / normal / high) per asset and timeframe, so you can see whether an edge only exists in one volatility regime. Answers "when does this work", not "does this work" — for the overall verdict use arena_get_strategy_insights, for the macro-regime cut arena_get_strategy_performance_by_regime, and for the raw volatility time series arena_get_volatility_history. Cells below min_trades are suppressed rather than shown as noise. [API Pro tier]

Is this pair calm or wild right now? Current ATR-based volatility phase (low/normal/high/expansion) per tracked pair, updated daily at 08:00 UTC. This is a single current state — for the time series behind it use arena_get_volatility_history, and for what the phase implies for strategy choice use arena_get_volatility_recommendations. Filter with `pair` when you only care about one asset instead of pulling all of them. [Free tier]

Which strategies suit the current volatility phase? Top-3 by historical win-rate for that phase on a given pair. Phase comes from the latest snapshot (arena_get_volatility_phases); minimum 20 trades per phase required for inclusion. Answers "which strategies did well in a phase like the current one?" — a historical ranking of what held up in comparable phases. Related: arena_get_volatility_phases (the phase itself), arena_get_edge_reports (filter effects with verdicts), validate_strategy (evidence check on a concrete configuration). [API Pro tier]

What are the strongest backtest results on the platform? Public leaderboard: the highest-CAGR backtest results across all users, with anonymized usernames, pair, strategy, interval and period. Answers "what has scored best on this platform so far". A top-of-leaderboard entry is the winner of a large search — pair it with arena_get_robustness_field or validate_strategy to see how much of the edge survives outside its winning cell. For the user's own runs use arena_list_backtests. [Free tier]

Do these two CAGR figures actually differ? Check before ranking them. Pass the two values as `a` and `b` (gross CAGR in percent, same basis) plus `axes` — which arbitrary choices went into them — and the tool returns whether their gap clears the MEASURED noise floor of those choices, along with the floor itself, the dominant axis, and the probe + date it was measured on. `axes` accepts: grid_phase (how a multi-day candle grid is aligned to the Unix epoch; exists only on 2d/3d), parameter_choice (neighbouring parameter settings — by far the largest axis), window_edges (shifting the start date), pair_selection (which pairs made it into the universe). Pass ALL axes that genuinely varied; the floor is their maximum, not their sum. Optionally set `interval` to the candle interval so the floor can be sharpened where an axis was measured per interval — passing grid_phase together with a non-multi-day `interval` is a hard error, because that axis does not exist there. `label_a` and `label_b` are optional display names for the two values and are echoed back inside the explanation, so a multi-way comparison stays readable. Read-only, no market data touched. [Free tier]

Which backtests have I run? Lists the backtest runs belonging to the authenticated user — newest first, with id, strategy, pair, interval, date range and headline metrics per run. Use it to find a run_id, then call arena_get_backtest for its detail or arena_get_backtest_trades for the individual trades. Only your OWN runs; for the public cross-user leaderboard use arena_get_winners. Paginated via limit + offset. [API Pro tier]

What knowledge objects exist here? Discover what Knowledge Objects exist: lists all published types + their subjects (with min_tier, api_path, seo_slug, latest as_of). Use this BEFORE arena_get_knowledge to learn valid type/subject pairs instead of guessing. New types appear automatically. [Free tier]

Which on-chain series are available? Lists all 65 available Bitcoin Research Kit (BRK) on-chain series across the groups pilot, sentiment, mining, supply, cointime, activity, liquidity (e.g. MVRV, NUPL, SOPR, Realized-Price, Mayer, Puell, STH/LTH SOPR, Hash-Ribbons). Returns id + label + group. Use the id with arena_get_onchain_latest / _history. [Free tier]

Which strategies can I backtest here? Lists all backtest strategies (key, label, plan, supported asset classes, primary indicators). Filterable by asset class and plan. Use this before calling arena_run_backtest to discover valid strategy names. Entries deprecated for an asset class stay listed (historical results depend on them) and carry deprecated_for + deprecation {since, reason} — do NOT call arena_run_backtest or validate_strategy for those combinations, they return 400. [Free tier]

Which alerts do I have running? Returns every ACTIVE subscription belonging to the current API key: id, type, trigger configuration, delivery method and expiry. Use it to see what is already running before creating a duplicate, and to get the subscription_id that arena_cancel_subscription needs. Does not return fired updates — that is arena_check_subscription_updates. [API Pro tier]

Which asset universes can I test against? Lists all crypto asset universes (BTC, top-10 crypto, top-50 crypto, etc.) — the underlying pair-sets used by custom-report and universe-backtest endpoints. [Free tier]

What would a custom report cost? Get a pricing quote for a custom report (universe-backtest PDF + Excel) without committing to a purchase. Returns price, universe size + preview, excluded pairs, and filter config. Crypto universes use top-N tiers (top-10 … top-250) or a custom pair list. [API Pro tier]

How would this strategy have performed? Run ONE strategy on ONE pair over a date range and get the full result: CAGR, total return, max drawdown, win-rate, trade count, Buy & Hold comparison, net-of-fees figures, and a run_id for later retrieval. Synchronous, typically 3–10s. Use this when the user wants a concrete result for a specific setup. For several strategies side by side use arena_compare_strategies; for many pairs at once use arena_run_universe_backtest; to judge whether an EXISTING result is trustworthy rather than produce a new one, use validate_strategy or arena_get_robustness_field. Filters are optional and only remove entries; run once without them for the baseline. Read result.benchmark before comparing cagr to buyhold_cagr: warmup or a late listing can shorten the strategy window, and matches_strategy_window:false means the two figures are annualized over DIFFERENT periods — in that case benchmark.strategy_window carries the like-for-like buy-and-hold over the window the strategy actually traded, and THAT is the one to compare against. Per-day quota: Pro=50, Power=500. [API Pro tier]

Would a grid bot have made money here? Simulate a GRID BOT (buy-low / sell-high ladder inside a fixed price range) on historical candles. Returns final value, return %, CAGR, trade count, fees paid and a Buy & Hold comparison. This is a different machine from the strategy backtester: grid bots earn from oscillation inside a range, not from trend — for signal-based strategies use arena_run_backtest instead. The result depends heavily on the range you choose (low_price / high_price); a range the price left early makes the bot idle, so treat range choice as part of the hypothesis, not a detail — arena_suggest_grid_range proposes a defensible range. Each run is saved to your account (the returned id is the run_id); publish a public snapshot page with arena_share_grid_backtest. grid_mode picks neutral (default) or long. Optional leverage (2/3/5, grid_mode long only, Pro) with funding_mode (conservative default / historical BTCUSDT / none): simulates an isolated-margin futures long grid — margin = total_investment, the grid trades margin × leverage, funding accrues daily on the open position, liquidation is checked per candle at the low. It simulates, it does not recommend: the result can be a total loss of the margin. Free tier limited to BTCUSDT/ETHUSDT. Per-day quota: Free=5, Pro=50, Power=500. [Free / Pro / Power tier]

Does this strategy hold up across a whole universe? Runs it against every pair in the universe. Pair cap depends on your API tier: Pro 50, Power 250 — Power covers crypto-top-250 in ONE job, and a single job keeps the ranking on one pair set (merging results across different pair sets measures pair selection, not strategy quality). THIS CALL IS ASYNCHRONOUS AND RETURNS NOTHING BUT A job_id: the result is NOT in this response. You MUST poll arena_get_job_status until status is 'completed'; estimated_seconds in the create-response says how long to budget. Provide either universe_id (call arena_list_universes) OR explicit pairs[]. Benchmarks bnh_fixed and dca_reference are accepted here — run one of them over the SAME universe and interval alongside: an excess over buy-and-hold is only readable next to the buy-and-hold value itself, which can be negative. beats_bh_count compares each pair's cagr against the LIKE-FOR-LIKE buy-and-hold — the benchmark measured over the window the strategy actually traded, not from the requested start. A long warmup or a pair listed after date_from shifts that start, and comparing across two different windows is a handicap, not a benchmark. The old pairing is still reported as beats_bh_count_requested_window, and pairs_with_window_offset says on how many pairs the two can differ at all; per-pair, buyhold_cagr_strategy_window and benchmark_matches_window carry the same distinction. PERSISTENCE: universe results live ONLY in the job response (api_jobs.result). They are deliberately not written to backtest_runs, so they carry no filter_binding and no coin-denominated history, and you will not find them later via arena_list_backtests — copy what you need out of the job result. Per-day quota: Pro=5, Power=50. [API Pro tier]

Want a public link for a grid result? Publishes a read-only snapshot page (with OG preview image) for one of YOUR saved grid backtest runs and returns its URL. Pass the run_id you got back from arena_run_grid_backtest (every authenticated run is saved automatically). The page shows the numbers the engine actually computed — this tool takes no result values, so shared pages cannot carry fabricated figures. Shares are permanent snapshots; language picks the page copy (en default). [Free tier]

Am I connected, and what can this key do? Returns auth status (key kind: oauth connector or bearer API key, tier), server version, current UTC time, and the rate-limit state (hour/day used, remaining, reset) WITHOUT consuming extra quota beyond this call itself. Call this first when other tools fail: it separates auth problems (reconnect), tier problems (upgrade) and rate limits (wait) from real outages. [Free tier]

Notify me when the bull-market stage count changes? Tracks the Bullmarket-Ampel active stage count (0–5). Optional direction filter (up/down/any) plus specific stages of interest. For the current stage count without subscribing, call arena_get_bullmarket_ampel. Creates a standing subscription; it does not return a value now — collect fired updates with arena_check_subscription_updates (polling) or receive them by webhook, and end it with arena_cancel_subscription. For the CURRENT value instead of a change notification, call the matching read tool. [API Pro tier and up — max 3 active subscriptions for Pro, 20 for Power]

Notify me when the cycle band changes? Bands: (capitulation → risk-off → neutral → constructive → euphoric). Optional bands filter restricts to specific target bands. For the current band without subscribing, call arena_get_cycle. Creates a standing subscription; it does not return a value now — collect fired updates with arena_check_subscription_updates (polling) or receive them by webhook, and end it with arena_cancel_subscription. For the CURRENT value instead of a change notification, call the matching read tool. [API Pro tier and up — max 3 active subscriptions for Pro, 20 for Power]

Notify me when market heat crosses a threshold? Fires when the daily 0–100 Arena-Pulse score crosses threshold_above (upward) or threshold_below (downward). At least one threshold is required. For the current score without subscribing, call arena_get_pulse. Creates a standing subscription; it does not return a value now — collect fired updates with arena_check_subscription_updates (polling) or receive them by webhook, and end it with arena_cancel_subscription. For the CURRENT value instead of a change notification, call the matching read tool. [API Pro tier and up — max 3 active subscriptions for Pro, 20 for Power]

Notify me when this signal flips? Fires when an existing Ampel-Config's signal flips (BUY ↔ SELL). Prerequisite: the user must have created that ampel-config in the web UI (`/dashboard/ampel`) — pass its UUID here; this tool cannot create one. Optional signal_types filter narrows to BUY-only or SELL-only. For the current signal state without subscribing, call arena_get_signal_status. Creates a standing subscription; it does not return a value now — collect fired updates with arena_check_subscription_updates (polling) or receive them by webhook, and end it with arena_cancel_subscription. For the CURRENT value instead of a change notification, call the matching read tool. [API Pro tier and up — max 3 active subscriptions for Pro, 20 for Power]

Which price range should my grid bot use? Answers the question arena_run_grid_backtest deliberately leaves open (its own description says: treat range choice as part of the hypothesis). Returns TWO independent range suggestions side by side: iv_anchored (BTCUSDT/ETHUSDT only — sized from option-implied volatility, Deribit DVOL Index; the methodically grounded answer) and recent_volatility (7 trading days of realized volatility before anchor_date — the method exchange auto-modes use, available for all pairs). When both exist and disagree strongly, the options market expects a different volatility regime than the recent past showed — that disagreement is information. Output field names (low_price, high_price, grid_count, grid_type) map 1:1 onto arena_run_grid_backtest inputs. Inputs: pair (required), anchor_date, sigma_mult (IV range width in sigmas, default 1.5), dte (IV horizon in days, default 30). For a historical backtest set anchor_date to your start_date so the volatility range matches the entry, not today; iv_anchored always reflects the latest IV snapshot regardless of anchor_date. Free tier limited to BTCUSDT/ETHUSDT (same gate as the grid backtest itself). [Free tier]

Does this strategy survive an honest test? Backtest a trading strategy honestly — look-ahead-aware validation with Deflated-Sharpe-Ratio / multiple-testing correction (Bailey & López de Prado). Returns an EVIDENCE verdict (insufficient_evidence | anecdote | failed_oos | passed_oos) plus metrics, flags and caveats — NOT a buy/sell recommendation. Call this before acting on a strategy or signal list. Accepts a named catalog strategy (type=rules), a timestamped BUY/SELL signal list (signal_list), or a timestamped trade list (trade_list). Checks: realistic next-bar fills (look-ahead/optimism), net of cost, out-of-sample split, and a hard 30-round-trip sample gate (under 30 is always "anecdote"). Not reproducible via generic backtest tools that ignore overfitting. [API Pro tier]