io.github.Schoasch/backtesting-arena

Est-ce que io.github.Schoasch/backtesting-arena est sûr ?

Au dernier audit MCP Hub, le serveur MCP « io.github.Schoasch/backtesting-arena » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 49 outils

• arena_batch≈ 415 tokens

  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]

  Paramètres, retour & exemple (1)

  Paramètres

  • queries · array · requis
    1-6 queries, executed in order.

  Exemple d'appel

  {"name": "arena_batch", "arguments": {"queries": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_call_extended≈ 1660 tokens

  Gateway to the EXTENDED tools of this server — listed here in one line each instead of individually, to keep the tool list short. Use it when no listed tool fits: per-metric daily history series, subscriptions, chart images, niche primitives. mode="call" runs the tool with `arguments` (same result, auth, tier and rate limits as calling it directly); mode="describe" returns its full description and parameters first, if the arguments are unclear. - arena_cancel_subscription: Stop this alert? - arena_check_subscription_updates: Has anything I subscribed to fired? - arena_cross_series: Did two market series move together, and what did BTC do next when they agreed or diverged? - arena_dca_scenario: Should I invest all at once or spread it out (DCA)? - arena_dip_scenario: Where would I add on a dip, and when is the thesis wrong? - arena_get_altcoin_season_history: Has capital been rotating into or out of altcoins? - arena_get_backtest_trades: Which trades did that backtest actually take? - arena_get_btc_macro_correlations: What does Bitcoin actually move with? - arena_get_carry_monitor: Which part of a week's spot-BTC-ETF inflows coincided with a short build-up by CME Leveraged Funds — i.e. - arena_get_chart: Renders one of the named platform series as a PNG line chart and returns it as an MCP image content block, plus a JSON … - arena_get_cost_basis_spread: Is the market in profit or at a loss? - arena_get_cycle_history: How has the cycle score moved over time? - arena_get_days_since_decline: How many days since Bitcoin last fell by X %? - arena_get_drift_log: Do two independent providers still agree on the same on-chain quantity? - arena_get_filter_insights: Do entry filters help, and which ones? - arena_get_funding_rate_history: How has leverage positioning shifted over time? - arena_get_gem_score: How does this altcoin score? - arena_get_gem_validation: Did the screener picks actually beat BTC? - arena_get_halvings: When were the halvings, and what followed? - arena_get_hash_ribbons: Are miners capitulating? - arena_get_kimchi_premium: Is Korean spot trading BTC at a premium? - arena_get_ma_distance_history: How far above or below its moving averages did price stand back then? - arena_get_mayer_multiple: Is BTC stretched against its 200-day average? - arena_get_mayer_multiple_history: How stretched has BTC been against its 200-day average? - arena_get_mean_reversion_index: How stretched is Bitcoin against its usual means — and does the popular "crossing 30 means the bottom is in" reading ho… - arena_get_ontology_term: What does this term mean here, exactly? - arena_get_pattern_scan: Is a classic chart formation (descending/ascending wedge, symmetric triangle, range) forming on a crypto pair right now… - arena_get_platform_activity: What are people backtesting right now? - arena_get_portfolio_correlation: Is my crypto portfolio actually diversified? - arena_get_pulse_history: How did market heat get to where it is? - arena_get_reference_models: How much does a Bitcoin valuation-model line depend on WHEN it was computed? - arena_get_report_status: Is my report ready? - arena_get_shared_backtest: What is in this shared backtest link? - arena_get_signal_events: When did which classic top/bottom signal actually flip? - arena_get_signal_status: Is this strategy signalling buy or sell right now? - arena_get_taker_imbalance: Are taker buys or taker sells dominating? - arena_get_trend_channels: Where does price sit inside its trend channel? - arena_get_volatility_insights: Does this strategy work better in calm or wild markets? - arena_get_volatility_phases: Is this pair calm or wild right now? - arena_get_volatility_recommendations: Which strategies suit the current volatility phase? - arena_get_volume_profile: WHERE was Bitcoin actually traded, as opposed to where it turned? - arena_get_winners: What are the strongest backtest results on the platform? - arena_list_subscriptions: Which alerts do I have running? - arena_quote_report: What would a custom report cost? - arena_share_grid_backtest: Want a public link for a grid result? - arena_subscribe_bullmarket_stage: Notify me when the bull-market stage count changes? - arena_subscribe_cycle_changes: Notify me when the cycle band changes? - arena_subscribe_pulse_changes: Notify me when market heat crosses a threshold? - arena_subscribe_signal_alerts: Notify me when this signal flips? - arena_suggest_grid_range: Which price range should my grid bot use?

  Paramètres, retour & exemple (3)

  Paramètres

  • mode · string · optionnel · call | describe
    "call" runs the tool; "describe" returns its description and input schema
  • tool · string · requis · arena_cancel_subscription | arena_check_subscription_updates | arena_cross_series | arena_dca_scenario | arena_dip_scenario | arena_get_altcoin_season_history | arena_get_backtest_trades | arena_get_btc_macro_correlations
    Name of the extended tool to run
  • arguments · object · optionnel
    Arguments for the tool, exactly as the tool itself takes them (mode="call")

  Exemple d'appel

  {"name": "arena_call_extended", "arguments": {"tool": "arena_cancel_subscription"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_compare_strategies≈ 1158 tokens

  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]

  Paramètres, retour & exemple (8)

  Paramètres

  • pair · string · requis
    Crypto pair symbol, e.g. BTCUSDT — the same pair for every strategy.
  • capital · number · optionnel
    Starting capital in quote currency. Default 10000. Affects absolute figures only, not CAGR or win-rate.
  • date_to · string · optionnel
    End date, YYYY-MM-DD. Default: today.
  • filters · object · optionnel
    Optional entry filters (Pro+). Each one only ever REMOVES entries — filters never create trades. Omit for the unfiltered baseline.
  • interval · string · requis · 1d | 2d | 3d | 1w | 1M
    Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • date_from · string · requis
    Start date, YYYY-MM-DD. Earlier than the pair listing is clamped to the first available candle.
  • asset_type · string · requis · crypto | tokenized_equity | tokenized_etf | commodities
    Asset class. Use 'crypto' unless you are explicitly backtesting a tokenized real-world asset. Note: tokenized stocks/ETFs/gold trade AS crypto pairs (e.g. spybUSDT, qqqbUSDT) — there is no separate stocks/forex backtest surface; non-crypto asset classes were retired.
  • strategies · array · requis
    The 2–5 strategies to compare, each with optional own params.

  Exemple d'appel

  {"name": "arena_compare_strategies", "arguments": {"pair": "texte", "interval": "1d", "date_from": "texte", "asset_type": "crypto", "strategies": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_dip_decision≈ 863 tokens

  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]

  Paramètres, retour & exemple (3)

  Paramètres

  • mode · string · optionnel · compare | allocate
    'compare' (default): EV of buy-now vs wait vs split + breakeven dip probability. 'allocate': risk-adjusted optimal deploy-now fraction under γ.
  • compare · object · optionnel
    Required when mode='compare'.
  • allocate · object · optionnel
    Required when mode='allocate'.

  Exemple d'appel

  {"name": "arena_dip_decision", "arguments": {"mode": "compare", "compare": {}, "allocate": {}}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_altcoin_season≈ 341 tokens

  Is it altcoin season? Daily Altcoin-Season indicator (v7 Native-Filter methodology). Returns BTC-, alt- and stablecoin dominance, the 4 Layer-1 signals (alt_dominance, total_market_cap, btc_dominance, stable_dominance — weekly smoothed-Heikin-Ashi directions; weights in methodology.layer1), overall color (red/yellow/green) and Layer 2: altcoin_season_pct (share of top-50 alts beating BTC over 90 days) with altcoin_season_pct_basis (beating / eligible / expected — coins without a 90-day return drop out of the denominator) and altcoin_season_pct_provenance (grade full/partial/thin; fields="full" names the missing coins), plus the top-30 CoinGecko snapshot. [Free tier]

  Paramètres, retour & exemple (1)

  Paramètres

  • fields · string · optionnel · full
    Omit for the default response, which carries `provenance` in short form (grade full/partial/thin, sources_used, sources_expected). 'full' adds the long form: sources_missing (named), sources (licence attribution names), grade_reason, as_of — plus per-part provenance where the composite has parts. A `thin` grade (under half the expected inputs) is never a basis for a verdict or a ranking.

  Exemple d'appel

  {"name": "arena_get_altcoin_season", "arguments": {"fields": "full"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_asset_snapshot≈ 550 tokens

  Where does this coin stand? ONE call per Binance USDT pair instead of six: last daily close, 7/30/90/365-day returns, relative strength vs BTC and vs ETH on the same horizons (with the MEASURED base rate next to it — the median altcoin loses against Bitcoin, so a positive number is a description, not an edge), the F6 trend state vs BTC, ATH/drawdown/days-since-ATH on the available exchange history (`ath_scope` says which), SMA200 distance, a `parabolic` state (in a parabolic run now? last run? plus what followed such runs per exit rule, from knowledge object parabolic_base_rate), realized 30d volatility and ATR%, liquidity from our own daily Binance universe measurement (24h-volume rank today vs 30 days ago, 30d mean/median volume, band), tokenomics ratios from the gem screener (Pro+, CoinGecko ratios only), derivatives (BTC only so far), `price_time_displacement` (how far back in time today’s close sits: days since first reached, share of days below today, in USD and vs BTC, plus the place in the daily ranking — full ranking in knowledge type ptd_ranking) and a `data_quality` block: history span, candle count, missing days, coverage %, source/stitch, listing status (delisted pairs are flagged) and a mechanical A/B/C grade whose rule travels in the payload. Every source can fail independently — sources_used / sources_unavailable make the basis auditable. Works for any Binance USDT pair, not just BTC/ETH/SOL. `detail: 'full'` adds the raw BTC/ETH benchmark returns behind the relative numbers. Descriptive, no signal. For the strategy-side question ("should I take this entry?") use arena_get_signal_context. [Free tier]

  Paramètres, retour & exemple (2)

  Paramètres

  • pair · string · requis
    Binance USDT pair, e.g. 'SOLUSDT'. Case-insensitive.
  • detail · string · optionnel · standard | full
    'standard' (default). 'full' adds the raw BTC/ETH benchmark returns used for the relative numbers.

  Exemple d'appel

  {"name": "arena_get_asset_snapshot", "arguments": {"pair": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_backtest≈ 198 tokens

  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]

  Paramètres, retour & exemple (1)

  Paramètres

  • id · string · requis
    UUID of the backtest run.

  Exemple d'appel

  {"name": "arena_get_backtest", "arguments": {"id": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_btc_market_structure≈ 218 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_get_btc_market_structure", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_bullmarket_ampel≈ 265 tokens

  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]

  Paramètres, retour & exemple (1)

  Paramètres

  • history_days · integer · optionnel
    Days of stage_history to return (1-365, default 30). Each row carries active_count plus all five per-stage booleans, so stage flips are readable per day instead of only via the derived `since` of the current run.

  Exemple d'appel

  {"name": "arena_get_bullmarket_ampel", "arguments": {"history_days": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_cycle≈ 791 tokens

  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 — each indicator’s 0-100 sub-score is in the score_* fields; the weights are in drivers[] under fields="full"; 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. Pass fields="full" (BTC) for `drivers[]`: each indicator sub-score with its renormalized weight, contribution (they add up to raw_score — the input of the z_adj_score that `signal` follows) and percentile_rank against its own history. Related: arena_get_historical_analog (what followed states like this one), arena_get_bullmarket_ampel, arena_get_pulse. [Free tier]

  Paramètres, retour & exemple (2)

  Paramètres

  • asset · string · optionnel · BTC | ETH | SOL
    Which asset’s cycle. Default BTC. ETH/SOL return a price-derived cycle read with not_applicable fields for BTC-native indicators.
  • fields · string · optionnel · full
    BTC only. 'full' adds `drivers[]` (indicator sub-scores with renormalized weight, contribution summing to raw_score, percentile_rank with basis and n) plus drivers_note.

  Exemple d'appel

  {"name": "arena_get_cycle", "arguments": {"asset": "BTC", "fields": "full"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_edge_reports≈ 684 tokens

  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]

  Paramètres, retour & exemple (5)

  Paramètres

  • limit · integer · optionnel
    Strategies per page (1–100). Omit for all.
  • market · string · requis · crypto | tokenized
    Market to analyze (crypto or tokenized).
  • offset · integer · optionnel
    Strategies to skip (paging).
  • verdict · string · optionnel · helps | neutral | hurts | insufficient_evidence | insufficient_data | all
    Filter by verdict. Default 'all'. Note 'insufficient_evidence' is NOT the same as 'insufficient_data': the former has enough runs but they disagree (the effect's 90% interval straddles the ±1pp line), the latter simply lacks runs.
  • strategy · string · optionnel
    Restrict to a single strategy key (e.g. golden_cross). Omit for all strategies.

  Exemple d'appel

  {"name": "arena_get_edge_reports", "arguments": {"market": "crypto"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_etf_flows≈ 807 tokens

  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 daily series (every US trading day: cumulative inflow + that day's net flow; `resolution` states points and spacing) 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. `source_inconsistencies` lists the days on which the source's own daily net flow and the change of its cumulative disagree (BTC: 2 of ~700 days, 2026-04-23/24 — the two-day sum matches, the day split does not); cumulative deltas and the 30d/90d windows are unaffected, per-day sums over those dates are. NYSE closing days that the source still lists come back with net_flow_usd_m null and market_closed: true (a closed market is not a zero flow). `availability` states when a day's flow was first known (measured from our own ingest; the source has no publication time) — use it for point-in-time work. For long windows pass `format: 'columns'` (parallel arrays, far smaller). Default BTC; pass asset=ETH, SOL, XRP, LTC, HBAR, DOGE, LINK, AVAX or DOT (US spot ETFs; the last seven have stored history only from 2026-09-21, so their 30d/90d windows are null until covered — `note` says so). No ZEC or NEAR ETF data. Source SoSoValue. [Free tier]

  Paramètres, retour & exemple (3)

  Paramètres

  • days · integer · optionnel
    Length of the returned daily series in days (every US trading day in the window). Default 365, clamped 7–1095.
  • asset · string · optionnel · BTC | ETH | SOL | XRP | LTC | HBAR | DOGE | LINK
    Which US spot-ETF flows. Default BTC. XRP/LTC/HBAR/DOGE/LINK/AVAX/DOT: stored history starts 2026-09-21 (grows daily).
  • format · string · optionnel · rows | columns
    Default 'rows' (series[] of objects). 'columns' returns series_columns instead — parallel arrays (date, cum_net_inflow_usd_m, net_flow_usd_m, market_closed) with each field name once; use it for long windows, it is much smaller.

  Exemple d'appel

  {"name": "arena_get_etf_flows", "arguments": {"days": 1, "asset": "BTC", "format": "rows"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_fear_greed≈ 584 tokens

  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+]

  Paramètres, retour & exemple (2)

  Paramètres

  • days · integer · optionnel
    How many daily readings to return (1-365, default 90). The full history since 2018 is deliberately not offered in one response — it is ~3,100 points and does not fit a tool response. The cap limits window SIZE, not position: combine with end_date to read any window since 2018.
  • end_date · string · optionnel
    Last day of the window (YYYY-MM-DD, inclusive). Positions the window anywhere in the history since 2018-02 — e.g. end_date=2025-10-06 answers "what was sentiment at the October 2025 top". Omit for a window ending today. value/classification/as_of describe the LAST day of the window; cadence/tempo (Pro+) compute on the history up to end_date only, never on later data.

  Exemple d'appel

  {"name": "arena_get_fear_greed", "arguments": {"days": 1, "end_date": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_funding_rate≈ 458 tokens

  Are longs or shorts paying right now? Latest BTC perpetual funding rate, averaged across up to 4 exchanges (Binance, Bybit, OKX, Deribit; 8h settlement cadence). Returns the day's averaged value plus each exchange's own value (no moving average or Z-score — for the history use arena_get_funding_rate_history via arena_call_extended). 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 (4 = 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. `provenance` grades the average (full = all exchanges, partial, thin = under half — never a basis for a verdict); fields="full" adds the venue spread in bps and the min/max interval of the venue values. A value of exactly 0.0001 (0.01 % per 8h) on many days is the exchanges' base-rate clamp on USDT perpetuals, not a cap in our pipeline: it means "no premium beyond the base rate", and values above it are real market readings. [Free tier]

  Paramètres, retour & exemple (1)

  Paramètres

  • fields · string · optionnel · full
    Omit for the default response, which carries `provenance` in short form (grade full/partial/thin, sources_used, sources_expected). 'full' adds the long form: sources_missing (named), sources (licence attribution names), grade_reason, as_of — plus per-part provenance where the composite has parts. A `thin` grade (under half the expected inputs) is never a basis for a verdict or a ranking.

  Exemple d'appel

  {"name": "arena_get_funding_rate", "arguments": {"fields": "full"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_gem_scores≈ 244 tokens

  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]

  Paramètres, retour & exemple (2)

  Paramètres

  • limit · integer · optionnel
    Number of coins to return (tier-capped)
  • from_rank · integer · optionnel
    Start from this rank (default 1)

  Exemple d'appel

  {"name": "arena_get_gem_scores", "arguments": {"limit": 1, "from_rank": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_historical_analog≈ 1201 tokens

  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. Set include_path=true (BTC, predicate presets) to add "path_statistics": what happened ON THE WAY — MAE/MFE, days to trough, days under water (total and longest unbroken streak), days to first recovery (null = never back within the horizon) — over the SAME episodes as forward_returns; below n=8 min/max are withheld because MAE is an extreme-value statistic. 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]

  Paramètres, retour & exemple (7)

  Paramètres

  • asset · string · optionnel · BTC | ETH | SOL
    Which asset’s cycle history. Default BTC. ETH/SOL support only price-derived presets (cycle_bottom_cluster, cycle_top_cluster).
  • preset · string · requis · cycle_bottom_cluster | cycle_top_cluster | deep_fear | euphoria | quiet_volatility
    Named ex-ante cycle-state condition set. One of: cycle_bottom_cluster, cycle_top_cluster, deep_fear, euphoria, quiet_volatility.
  • include_path · boolean · optionnel
    Default false. true adds path_statistics per horizon (MAE/MFE, days under water, longest under-water streak, days to first recovery) over the same episodes as forward_returns. BTC and predicate presets only (not quiet_volatility, not ETH/SOL in v1).
  • forward_horizons · array · optionnel
    Forward-return horizons in days. Default [30, 90, 180, 365] — except for quiet_volatility, which defaults to the horizons its study actually tested ([30, 90, 180]); anything beyond that is flagged as outside the protocol.
  • vol_rank_threshold · number · optionnel · 5 | 10 | 20 | 50
    quiet_volatility only. Reference threshold as a FIXED step: 50 (default, below trailing median — the studied definition) or 5/10/20 (unusually quiet: RV30 below its trailing Nth percentile). Any value other than 50 sets study_finding_applies=false — the null study covered only the default.
  • direction_window_days · integer · optionnel
    Classification window for condition_on_direction (default 5). Only meaningful together with condition_on_direction.
  • condition_on_direction · string · optionnel · up | down
    quiet_volatility only. Condition episodes on the direction of the FIRST post-anchor move (sign of the direction_window_days-day return). Horizons <= direction_window_days are suppressed as circular. Sets study_finding_applies=false.

  Exemple d'appel

  {"name": "arena_get_historical_analog", "arguments": {"preset": "cycle_bottom_cluster"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_indicator_snapshot≈ 524 tokens

  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). It also carries `rsi_14_4w` (RSI-14 on 28-day candles, with `bars` — few, so crossing counts stay small; its source_note states what followed crossings). `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]

  Paramètres, retour & exemple (2)

  Paramètres

  • pair · string · optionnel
    Pair, e.g. "BTCUSDT" (default), "ETHUSDT", "PAXGUSDT".
  • interval · string · optionnel · 1d | 1w
    Default '1d'. '1w' computes every indicator on weekly bars.

  Exemple d'appel

  {"name": "arena_get_indicator_snapshot", "arguments": {"pair": "texte", "interval": "1d"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_iv_snapshot≈ 432 tokens

  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]

  Paramètres, retour & exemple (2)

  Paramètres

  • currency · string · requis · BTC | ETH
    Currency to fetch IV snapshot for
  • include_implied · boolean · optionnel
    Default false (response unchanged). When true, adds an `implied` block with the FORWARD volatility risk premium, its percentile and the historical base rate.

  Exemple d'appel

  {"name": "arena_get_iv_snapshot", "arguments": {"currency": "BTC"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_job_status≈ 160 tokens

  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]

  Paramètres, retour & exemple (1)

  Paramètres

  • job_id · string · requis
    UUID job_id returned by arena_run_universe_backtest.

  Exemple d'appel

  {"name": "arena_get_job_status", "arguments": {"job_id": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_key_levels≈ 248 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_get_key_levels", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_knowledge≈ 339 tokens

  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]

  Paramètres, retour & exemple (4)

  Paramètres

  • type · string · requis
    Knowledge object type, e.g. 'market_regime'.
  • as_of · string · optionnel
    Specific date YYYY-MM-DD. Omit for latest.
  • subject · string · requis
    Subject ref, e.g. 'GLOBAL', 'BTC'.
  • include_graph · boolean · optionnel
    If true, attach the resolved edge neighbourhood (outbound + inbound) for graph traversal.

  Exemple d'appel

  {"name": "arena_get_knowledge", "arguments": {"type": "texte", "subject": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_macro_regime≈ 721 tokens

  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. `fed_funds_rate` is FRED FEDFUNDS, the monthly AVERAGE effective rate (lags; not the daily DFF, not the target range); `global_m2_yoy` is NOT M2 but the YoY change of G3 central-bank balance sheets (Fed+ECB+BoJ in USD) — it can fall while US M2 hits a record. component_notes carries these definitions in the payload. 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). `provenance` says how many of the 18 weighted components formed today’s composite (grade full/partial/thin; null for rows written before 2026-10-13, when coverage was not recorded); missing components drop out and the tier renormalizes, a tier with no input at all scores a stand-in 50 and is named in `degraded.empty_tiers`. Pass fields="full" for the named missing components, sources and per-tier provenance. `move_index` and `fed_funds_cuts_priced` are always null and never weighted — slots without a source, not outages. [Free tier]

  Paramètres, retour & exemple (1)

  Paramètres

  • fields · string · optionnel · full
    Omit for the default response, which carries `provenance` in short form (grade full/partial/thin, sources_used, sources_expected). 'full' adds the long form: sources_missing (named), sources (licence attribution names), grade_reason, as_of — plus per-part provenance where the composite has parts. A `thin` grade (under half the expected inputs) is never a basis for a verdict or a ranking.

  Exemple d'appel

  {"name": "arena_get_macro_regime", "arguments": {"fields": "full"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_max_pain≈ 1310 tokens

  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). With ladder or GEX, pass `expiry_date` to get ONE expiry instead of all upcoming ones — the full ten-expiry response with both is large. 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]

  Paramètres, retour & exemple (4)

  Paramètres

  • market · string · optionnel · DERIBIT_BTC
    Options market. Only DERIBIT_BTC is served: IBIT (BlackRock spot-ETF options) is still collected daily but no longer delivered — the chain comes from an unlicensed source, so it cannot be redistributed (2026-09-24).
  • expiry_date · string · optionnel
    Optional YYYY-MM-DD of ONE open expiry: upcoming[] (and its strike_ladder/gex) is restricted to it, which keeps ladder/GEX responses small; gex_totals still covers the whole book. An expiry that is not open returns invalid_input listing the open ones. Omit for all upcoming expiries.
  • include_gex · boolean · optionnel
    Default false (response unchanged). DERIBIT_BTC only. When true, each upcoming expiry carries a `gex` block plus `gex_totals` across the whole book: Black-Scholes gamma notional (USD per 1 % spot move) per 2.5 % band from LIVE Deribit mark IV per strike (gex_data_as_of names the fetch, ~10 min cache — a different observation time than the 02:00 UTC snapshot fields). The dealer SIGN is an assumption, not a measurement: both conventions are published side by side (assuming_dealers_short_all, assuming_squeezemetrics_convention); where they disagree, the data does not know the answer. zero_gamma_level flips only under the SqueezeMetrics convention — short-all is <= 0 everywhere and has no zero crossing by construction (its null is structural; zero_gamma_level.note says so). Tau floor 2 h near expiry (tau_clamped flags it); instruments without usable IV are excluded and counted.
  • include_strike_ladder · boolean · optionnel
    Default false (response unchanged). When true, every expiry carries a `strike_ladder`: open interest per 2.5 % price band around the snapshot spot (±25 %, calls/puts separate, absolute contracts, share_pct), below_range/above_range sums, max_pain_recomputed (cross-check against the stored level) and `delta` vs the previous day's snapshot on the same band grid (null with delta_reason when there is none). OI is a stock, not a side — no hedge direction follows; the note travels with the response.

  Exemple d'appel

  {"name": "arena_get_max_pain", "arguments": {"market": "DERIBIT_BTC", "expiry_date": "texte", "include_gex": true, "include_strike_ladder": true}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_max_pain_history≈ 693 tokens

  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]

  Paramètres, retour & exemple (6)

  Paramètres

  • days · integer · optionnel
    Days back from today (default 90, capped by tier).
  • market · string · optionnel · DERIBIT_BTC
    Options market. Only DERIBIT_BTC is served: IBIT (BlackRock spot-ETF options) is still collected daily but no longer delivered — the chain comes from an unlicensed source, so it cannot be redistributed (2026-09-24).
  • expiry_type · string · optionnel · daily | weekly | monthly | quarterly
    Filter expiries AND open_snapshots to one expiry class (label = highest level reached; the nesting booleans stay untouched). base_rates are always computed BEFORE this filter.
  • min_contracts · integer · optionnel
    Only finalized expiries with total_contracts >= this (rows with unknown contracts drop out when set).
  • snapshot_expiry_date · string · optionnel
    Reduce open_snapshots[] to exactly this expiry date (YYYY-MM-DD). Only meaningful with include_open_snapshots=true.
  • include_open_snapshots · boolean · optionnel
    Default false. When true, adds open_snapshots[] (daily observations of not-yet-expired contracts) plus open_snapshot_coverage. Omit for the unchanged response.

  Exemple d'appel

  {"name": "arena_get_max_pain_history", "arguments": {"days": 1, "market": "DERIBIT_BTC", "expiry_type": "daily", "min_contracts": 1, "snapshot_expiry_date": "texte", "include_open_snapshots": true}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_onchain_history≈ 302 tokens

  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]

  Paramètres, retour & exemple (2)

  Paramètres

  • days · integer · optionnel
    Days back from today (clamped by tier).
  • series_id · string · requis
    BRK series id, e.g. 'mvrv'.

  Exemple d'appel

  {"name": "arena_get_onchain_history", "arguments": {"series_id": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_onchain_latest≈ 222 tokens

  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]

  Paramètres, retour & exemple (1)

  Paramètres

  • series_id · string · requis
    BRK series id, e.g. 'mvrv', 'sopr', 'realized_price'.

  Exemple d'appel

  {"name": "arena_get_onchain_latest", "arguments": {"series_id": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_pulse≈ 364 tokens

  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. band_driver names the number the band follows. Pass fields="full" for `drivers[]`: each component with value, weight, contribution (they add up to the score) and percentile_rank against its own history. [Free tier]

  Paramètres, retour & exemple (1)

  Paramètres

  • fields · string · optionnel · full
    Omit for the default response. 'full' adds `drivers[]` — each component with value, weight, contribution (the contributions add up to the headline score), percentile_rank against its own stored history (expanding, with basis and n) — plus drivers_note with the exact arithmetic.

  Exemple d'appel

  {"name": "arena_get_pulse", "arguments": {"fields": "full"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_robustness_field≈ 605 tokens

  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]

  Paramètres, retour & exemple (8)

  Paramètres

  • axis · string · optionnel · cross_asset | parameter
    Neighborhood axis. 'cross_asset' (default, dense, carries the verdict + DSR-N) or 'parameter' (secondary, always anecdotal — the parameter settings tested on this one pair).
  • pair · string · requis
    Trading pair of your cell, e.g. 'BTCUSDT'.
  • params · object · optionnel
    Optional: numeric strategy parameters of your cell. Only numeric params define the neighborhood; matched per pair where the factory ran them.
  • interval · string · requis
    Candle interval, e.g. '1d', '1w', '1M'.
  • strategy · string · requis
    Strategy key, e.g. 'rsi_sma'.
  • user_cagr · number · requis
    Your result: CAGR in percent (e.g. 41 for +41%) — the cell being assessed.
  • asset_type · string · optionnel
    Asset class filter (default 'crypto').
  • user_sharpe · number · optionnel
    Optional: your annualized Sharpe (result_sharpe scale). Used for the counted-N Deflated Sharpe where neighbor coverage allows.

  Exemple d'appel

  {"name": "arena_get_robustness_field", "arguments": {"pair": "texte", "interval": "texte", "strategy": "texte", "user_cagr": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_signal_context≈ 671 tokens

  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]

  Paramètres, retour & exemple (4)

  Paramètres

  • pair · string · requis
    Pair, e.g. 'BTCUSDT'. Case-insensitive.
  • detail · string · optionnel · headline | full
    'headline' (default): statement + key numbers + decisive filters. 'full': every measured variant plus the raw source blocks.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Default '1w'. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key, e.g. 'rsi_sma'. See arena_list_strategies.

  Exemple d'appel

  {"name": "arena_get_signal_context", "arguments": {"pair": "texte", "strategy": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_spot_price≈ 120 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_get_spot_price", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_stablecoin_supply≈ 293 tokens

  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 daily time series (`days`, default 365; `resolution` states points and spacing) 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]

  Paramètres, retour & exemple (1)

  Paramètres

  • days · integer · optionnel
    Length of the returned daily series in days. Default 365, clamped 7–1095.

  Exemple d'appel

  {"name": "arena_get_stablecoin_supply", "arguments": {"days": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_sth_cost_basis≈ 270 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_get_sth_cost_basis", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_strategy_filter_effect≈ 456 tokens

  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]

  Paramètres, retour & exemple (3)

  Paramètres

  • asset · string · requis
    Pair / symbol (e.g. BTCUSDT). Case-insensitive.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Default '1w'. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key (see arena_get_strategies).

  Exemple d'appel

  {"name": "arena_get_strategy_filter_effect", "arguments": {"asset": "texte", "strategy": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_strategy_insights≈ 707 tokens

  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, dca_reference_v2); 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. Counts: `runCount` = deduplicated runs above the trade floor that carry the averages, `inertRuns` = 0-trade runs of the same cell counted IN ADDITION, `runs_total` = both. `avgWinRate` averages only runs with a rated trade (0-trade runs and open single positions store 0, which is not a hit rate); `avgBuyholdCagr`/`beatsBuyhold` are shown only when >= 50 % of the cell's assets carry the strategy-window benchmark (`bhAssetCoverage`) — the envelope carries benchmark_definition, win_rate_definition and counts_definition. Matrix mode also carries `summary` (cells_total, cells_with_benchmark, cells_beating_bh, cells_beating_bh_with_negative_cagr, cells_without_cagr) counted over the cells actually returned — quote those instead of counting cells yourself. [Free: 6 strategies / Pro+: full]

  Paramètres, retour & exemple (6)

  Paramètres

  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Detail mode: interval. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • min_runs · integer · optionnel
    Matrix mode: minimum runs per cell. Default 5.
  • strategy · string · optionnel
    Detail mode: strategy key (used together with `interval`).
  • asset_type · string · optionnel · crypto | tokenized_equity | tokenized_etf | commodities
    Restrict to one asset class.
  • assets_mode · string · optionnel · all | top10
    'top10' restricts to top-10 pairs by run-count.
  • ref_strategy · string · optionnel · bh | dca
    Benchmark reference. Default 'bh'.

  Exemple d'appel

  {"name": "arena_get_strategy_insights", "arguments": {"interval": "1d", "min_runs": 1, "strategy": "texte", "asset_type": "crypto", "assets_mode": "all", "ref_strategy": "bh"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_strategy_performance≈ 484 tokens

  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]

  Paramètres, retour & exemple (5)

  Paramètres

  • asset · string · requis
    Crypto pair / symbol (e.g. BTCUSDT, ETHUSDT). Case-insensitive.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Default '1w'. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key (e.g. rsi_sma, golden_cross). See arena_get_strategies for valid keys.
  • asset_type · string · optionnel · crypto | tokenized_equity | tokenized_etf | commodities
    Optional asset class filter to disambiguate (e.g. when same pair-name exists in two classes).
  • ref_strategy · string · optionnel · bh | dca
    Benchmark reference. Default 'bh' (Buy & Hold).

  Exemple d'appel

  {"name": "arena_get_strategy_performance", "arguments": {"asset": "texte", "strategy": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_strategy_performance_by_regime≈ 607 tokens

  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]

  Paramètres, retour & exemple (4)

  Paramètres

  • asset · string · requis
    Crypto pair / symbol (e.g. BTCUSDT, ETHUSDT). Case-insensitive.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Default '1w'. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key (e.g. rsi_sma, golden_cross). See arena_get_strategies.
  • asset_type · string · optionnel · crypto | tokenized_equity | tokenized_etf | commodities
    Optional asset class filter to disambiguate identical pair-names.

  Exemple d'appel

  {"name": "arena_get_strategy_performance_by_regime", "arguments": {"asset": "texte", "strategy": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_universe≈ 354 tokens

  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. Without as_of the universe reflects the CURRENT membership (CoinGecko market-cap rank) — a backtest over it carries survivorship bias for the earlier years; the `pit` block in the payload says so. With as_of (YYYY-MM-DD, >= 2026-07-14) it returns the membership as MEASURED on that day from our own daily record of Binance USDT spot, ranked by 24h quote volume (not market cap) — coins delisted since are included, coins listed later are not. Point-in-time universes are recorded forward-only; earlier dates are refused, not reconstructed. [Free tier]

  Paramètres, retour & exemple (2)

  Paramètres

  • as_of · string · optionnel
    Optional YYYY-MM-DD. Point-in-time membership on that day (recorded since 2026-07-14, volume-ranked).
  • universe_id · string · requis
    Universe id, e.g. 'crypto-top-50'.

  Exemple d'appel

  {"name": "arena_get_universe", "arguments": {"universe_id": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_get_volatility_history≈ 950 tokens

  How volatile has Bitcoin been? Daily Bitcoin volatility time series: realized volatility (30d & 90d, √365-annualized — calendar days, the same basis as DVOL — 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 `format: "columns"` (one array per field — the largest saving) together with `schema_version: "2026-08"` (rounds floats; opt-in until the default flips 2026-11-01), `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]

  Paramètres, retour & exemple (8)

  Paramètres

  • to · string · optionnel
    ISO date (YYYY-MM-DD), inclusive. End of the window. Defaults to the latest bar.
  • days · integer · optionnel
    Number of most recent days to return. Free tier capped at 365; API Pro unlimited. Ignored when from/to are given.
  • from · string · optionnel
    ISO date (YYYY-MM-DD), inclusive. Start of the window. Free tier still only sees the last 365 days.
  • meta · string · optionnel · full | minimal
    Default full. 'minimal' drops params/params_hash/warmup, which are only useful on the first call.
  • fields · string · optionnel · minimal | full
    Default full. 'minimal' returns date, close, rv, rvRank, rvRankRolling, atrPctAnnRank, atrPctAnnRankRolling only — measured saving 18–20 % of characters (full-history series, 2026-07-31; the `size` block in the response has the figure for your actual call), not a fifth of the size. Combine with granularity or a from/to window for a real reduction; dropping fields alone saves less than it looks.
  • format · string · optionnel · rows | columns
    Default 'rows' (series[] of objects). 'columns' returns series_columns instead — one array per field (date, close, rv, …) with each field name written once; the largest single saving for long ranges. Combines with fields, granularity and schema_version (the size block then measures the combined saving).
  • granularity · string · optionnel · daily | weekly | monthly
    Default daily. weekly/monthly keep the LAST observation of each period (a state, not an average).
  • schema_version · string · optionnel · 2026-07 | 2026-08
    Default '2026-07' (unchanged output). '2026-08' rounds floats to 2 decimals (ranks 1) and reports the saving. Default flips 2026-11-01.

  Exemple d'appel

  {"name": "arena_get_volatility_history", "arguments": {"to": "texte", "days": 1, "from": "texte", "meta": "full", "fields": "minimal", "format": "rows"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_is_distinguishable≈ 758 tokens

  Do these two backtest figures actually differ? Check before ranking them. Pass the two values as `a` and `b` (same metric, 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. `metric` selects the figure being compared: cagr (default, gross CAGR in percent), max_drawdown (bar-wise max drawdown in percent) or coin_vs_hold (percent more or fewer coins than buying and holding from the start). Each has its own measured floors; grid_phase is measured for cagr only and errors on the others. Read-only, no market data touched. [Free tier]

  Paramètres, retour & exemple (7)

  Paramètres

  • a · number · requis
    First value in percent of the chosen metric (e.g. 33.1 for +33.1% CAGR).
  • b · number · requis
    Second value, same metric, same unit and same basis as a.
  • axes · array · requis
    Which arbitrary choices differ between a and b. Pass every one that genuinely varied — omitting an axis makes the answer look more certain than it is.
  • metric · string · optionnel · cagr | max_drawdown | coin_vs_hold
    Which figure a and b are: cagr (default), max_drawdown or coin_vs_hold. Floors differ by metric.
  • label_a · string · optionnel
    Optional name for a, echoed in the explanation.
  • label_b · string · optionnel
    Optional name for b, echoed in the explanation.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Candle interval, if known (e.g. '1d', '2d', '3d', '1w'). Sharpens the floor where an axis was measured per interval. Passing grid_phase with a non-multi-day interval is an error, not a rounding detail — that axis does not exist there.

  Exemple d'appel

  {"name": "arena_is_distinguishable", "arguments": {"a": 1, "b": 1, "axes": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_list_backtests≈ 442 tokens

  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]

  Paramètres, retour & exemple (6)

  Paramètres

  • pair · string · optionnel
    Filter by pair symbol, e.g. BTCUSDT. Omit for all.
  • limit · integer · optionnel
    Page size, max 100, default 50.
  • offset · integer · optionnel
    Rows to skip for paging; default 0.
  • interval · string · optionnel · 1d | 2d | 3d | 1w | 1M
    Filter by candle interval; omit for all. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · optionnel
    Filter by strategy key, e.g. 'rsi_sma'. Omit for all.
  • asset_type · string · optionnel · crypto | tokenized_equity | tokenized_etf | commodities
    Filter by asset class, e.g. 'crypto'. Omit for all.

  Exemple d'appel

  {"name": "arena_list_backtests", "arguments": {"pair": "texte", "limit": 1, "offset": 1, "interval": "1d", "strategy": "texte", "asset_type": "crypto"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_list_knowledge≈ 126 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_list_knowledge", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_list_onchain_series≈ 129 tokens

  Which on-chain series are available? Lists all 71 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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_list_onchain_series", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_list_strategies≈ 280 tokens

  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]

  Paramètres, retour & exemple (3)

  Paramètres

  • lang · string · optionnel · de | en
    Localized names/taglines. Default 'en'.
  • plan · string · optionnel · free | pro | elite
    Filter to strategies of this plan tier.
  • asset_class · string · optionnel · crypto | tokenized_rwa
    Filter to strategies supporting this asset class (crypto or tokenized_rwa).

  Exemple d'appel

  {"name": "arena_list_strategies", "arguments": {"lang": "de", "plan": "free", "asset_class": "crypto"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_list_universes≈ 87 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_list_universes", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_run_backtest≈ 1190 tokens

  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 (its cagr_delta_pp is benchmark-minus-benchmark, defined in cagr_delta_pp_definition; strategy vs like-for-like benchmark is strategy_minus_window_benchmark_pp) 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]

  Paramètres, retour & exemple (9)

  Paramètres

  • pair · string · requis
    Crypto pair symbol, e.g. BTCUSDT, ETHUSDT, SOLUSDT.
  • params · object · optionnel
    Strategy-specific parameters, e.g. { rsi_period: 14 }. Omit to use the audited defaults — changing them without a reason is how overfitting starts.
  • capital · number · optionnel
    Starting capital in quote currency. Default 10000. Affects absolute figures only, not CAGR or win-rate.
  • date_to · string · optionnel
    End date, YYYY-MM-DD. Default: today.
  • filters · object · optionnel
    Optional entry filters (Pro+). Each one only ever REMOVES entries — filters never create trades. Omit for the unfiltered baseline.
  • interval · string · requis · 1d | 2d | 3d | 1w | 1M
    Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key — use arena_list_strategies to find valid keys.
  • date_from · string · requis
    Start date, YYYY-MM-DD. Earlier than the pair listing is clamped to the first available candle.
  • asset_type · string · requis · crypto | tokenized_equity | tokenized_etf | commodities
    Asset class. Use 'crypto' unless you are explicitly backtesting a tokenized real-world asset. Note: tokenized stocks/ETFs/gold trade AS crypto pairs (e.g. spybUSDT, qqqbUSDT) — there is no separate stocks/forex backtest surface; non-crypto asset classes were retired.

  Exemple d'appel

  {"name": "arena_run_backtest", "arguments": {"pair": "texte", "interval": "1d", "strategy": "texte", "date_from": "texte", "asset_type": "crypto"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_run_grid_backtest≈ 1637 tokens

  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 — plus `zerlegung` (spot runs): `decomposition` splits the result into ladder P&L from completed buy→sell cycles, allocation P&L of the starting coins, open grid buys and fees (identity_check_usd must be ~0); `benchmarks` anchors buy-and-hold, the never-touched starting split (static_allocation, with coin_share_start) and an arithmetic 50/50 at the entry price — grid_vs_static_pp is the number that says whether the ladder added anything over just holding the split; `fee_economics` gives the break-even spacing (2 × fee) and flags below_breakeven. Note: buyhold_return/outperformance keep their legacy anchor (first→last candle close); benchmarks anchor at entry. 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 (by default the grid pauses outside the range and resumes when price returns; set stop_on_range_exit to end the run at the first close outside it instead, selling all coins there), 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, by default opening an initial position at the start like Binance/KuCoin futures grids (start_rule), 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]

  Paramètres, retour & exemple (17)

  Paramètres

  • pair · string · requis
    Crypto pair symbol, e.g. BTCUSDT. Free tier: BTCUSDT or ETHUSDT only.
  • end_date · string · requis
    Simulation end, YYYY-MM-DD.
  • fee_rate · number · requis
    Per-trade fee fraction, e.g. 0.001 for 0.1% (Binance spot taker).
  • leverage · number · optionnel · 1 | 2 | 3 | 5
    Optional, default 1 (spot grid, unchanged). 2/3/5 = isolated-margin long grid (grid_mode must be long; Pro). Adds liquidated, liquidation_time/price, funding_cost_usd and max_notional_exposure to the result; final_value/total_return are then on the margin.
  • grid_mode · string · optionnel · neutral | long
    'neutral' (default): buys coins for every level above the entry at the start (the coin share follows the entry's position in the range — measured 2–83 %, NOT a fixed 50/50; see benchmarks.static_allocation.coin_share_start), then buys and sells around the entry. 'long': starts 100% in cash, buys dips below the entry, sells on recovery — required for leverage (with leverage the start follows start_rule).
  • grid_type · string · requis · arithmetic | geometric
    Level spacing: 'arithmetic' = equal price steps, 'geometric' = equal percentage steps (usually the better fit for crypto).
  • low_price · number · requis
    Lower bound of the grid range, in quote currency. Below it the bot is fully invested and stops buying.
  • grid_count · integer · requis
    Number of grid levels between low_price and high_price (2–200). More levels = more, smaller trades = more fees.
  • high_price · number · requis
    Upper bound of the grid range, in quote currency. Above it the bot is fully in cash and stops selling. Must exceed low_price.
  • start_date · string · requis
    Simulation start, YYYY-MM-DD.
  • start_rule · string · optionnel · initial_position | cash
    Only with leverage > 1. 'initial_position' (default, exchange rule of a futures long grid): at the start the bot buys one grid amount for every level above the entry at the entry price, so it holds a position — and carries funding and liquidation risk — from the first candle. 'cash': starts 100% in cash and only buys once price falls to a buy level (idle after a rising start).
  • entry_price · number · optionnel
    Optional price at which the bot starts; default is the OPEN of the first candle (must lie inside low_price..high_price).
  • funding_mode · string · optionnel · none | conservative | historical
    Only with leverage > 1. 'conservative' (default): flat 0.05%/day on the open position. 'historical': recorded daily average of three exchanges, BTCUSDT from 2019-09-08 only — otherwise falls back to conservative and flags funding_fell_back_to_conservative. 'none': no funding (optimistic).
  • stop_loss_price · number · optionnel
    Optional: liquidate the whole grid and stop once price falls to this level.
  • total_investment · number · requis
    Capital in USDT spread across the grid; min 100.
  • take_profit_price · number · optionnel
    Optional: liquidate the whole grid and stop once price rises to this level.
  • stop_on_range_exit · boolean · optionnel
    Optional (default false): stop once a candle CLOSES outside low_price..high_price — sell all coins at that close, stop_reason range_exit_high/low. Default: the grid pauses outside the range and resumes when price returns.

  Exemple d'appel

  {"name": "arena_run_grid_backtest", "arguments": {"pair": "texte", "end_date": "texte", "fee_rate": 1, "grid_type": "arithmetic", "low_price": 1, "grid_count": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_run_universe_backtest≈ 1280 tokens

  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_v2 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]

  Paramètres, retour & exemple (9)

  Paramètres

  • pairs · array · optionnel
    Explicit pair list. Hard schema limit 250; the effective cap is your tier (Pro 50, Power 250). Use instead of universe_id.
  • params · object · optionnel
    Strategy-specific parameters applied to EVERY pair in the universe. Omit for audited defaults.
  • capital · number · optionnel
    Starting capital in quote currency. Default 10000. Affects absolute figures only, not CAGR or win-rate.
  • date_to · string · optionnel
    End date, YYYY-MM-DD. Default: today.
  • filters · object · optionnel
    Optional entry filters (Pro+). Each one only ever REMOVES entries — filters never create trades. Omit for the unfiltered baseline.
  • interval · string · requis · 1d | 2d | 3d | 1w | 1M
    Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
  • strategy · string · requis
    Strategy key — call arena_list_strategies.
  • date_from · string · requis
    Start date, YYYY-MM-DD. Earlier than the pair listing is clamped to the first available candle.
  • universe_id · string · optionnel
    Pre-curated universe — call arena_list_universes for valid IDs. Capped by tier (Pro 50, Power 250); a larger universe is rejected rather than silently truncated.

  Exemple d'appel

  {"name": "arena_run_universe_backtest", "arguments": {"interval": "1d", "strategy": "texte", "date_from": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• arena_status≈ 140 tokens

  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]

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "arena_status", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• validate_strategy≈ 1318 tokens

  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"). signal_list/trade_list are re-priced at the OPEN of the bar after each timestamp (fill_rule: next_bar_open); their max_drawdown is measured on closed-trade equity (max_drawdown_basis), so it reads shallower than the daily mark-to-market drawdown of arena_run_backtest. Flag below_buyhold = net return under the single-entry buy & hold in the payload (informational, not part of the verdict). Not reproducible via generic backtest tools that ignore overfitting. [API Pro tier]

  Paramètres, retour & exemple (5)

  Paramètres

  • oos · object · optionnel
    How the claim is tested out-of-sample. Omit for the default split — the out-of-sample part is what separates a finding from a fit.
  • costs · object · optionnel
    Trading costs. Default 10 bps (crypto) / 5 bps (else) — a gross-only claim usually shrinks once these apply.
  • market · object · requis
    Which market the claim is about — prices are re-fetched from here, not taken from you.
  • window · object · requis
    Period over which the claim is checked.
  • strategy · object · requis
    The claim being validated — supply exactly one of: a catalog strategy (type=rules), your signals (type=signal_list) or your finished trades (type=trade_list).

  Exemple d'appel

  {"name": "validate_strategy", "arguments": {"market": {}, "window": {}, "strategy": {}}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://tradingstrategies.work/api/mcp