Hypotheses

Test whether improving trading liquidity predicts multi-day relative appreciation across COMP, CRV, DYDX and SNX perpetuals. Use synchronized 4-HOUR OHLCV collected by HyperliquidCollector. Exact catalog inspection verified 5,897 common timestamps from 2024-01-22T12:00:00Z through 2026-10-02T00:00:00Z, versus 5,902 expected grid points. Trade one equal-dollar long-short pair at a time. The mechanism is a change in within-token return-per-dollar-volume, rather than price momentum, spread mean reversion or funding carry. Amihud's liquidity measure supplies the measurement idea; its equity evidence does not establish this crypto hypothesis. Source: https://doi.org/10.1016/S1386-4181(01)00024-6. Implement with bounded rolling buffers and FactoryStrategy multi-instrument hooks.

Test whether substantial three-day LTC-versus-BCH price divergence reverses when neither asset attracts above-normal trading volume. Use synchronized 4-HOUR perpetual bars, buying the relative loser and shorting the winner with equal dollar notionals. HyperliquidCollector supplies OHLCV for both exact instruments. Catalog inspection verified 5,883 matching timestamps covering the complete shared four-hour grid from 2024-01-25 16:00 UTC through 2026-10-02 00:00 UTC, with timestamp markers present. Library inspiration: https://www.fmz.com/strategy/426047, whose price-volume interpretation motivates distinguishing low-participation moves; this proposal does not copy its RSI/Bollinger implementation. Research background: [Dynamic Volume-Return Relation](https://www.nber.org/papers/w8312) distinguishes risk-sharing reversal from information-driven continuation. Applying that distinction to these crypto perpetuals is an unvalidated hypothesis.

Cross-sectional, long-short, dollar-neutral equity strategy on USEQ daily bars, using OHLCV only. Each daily bar carries the overnight leg (open_t / close_{t-1}) and the intraday leg (close_t / open_t). Akbas, Boehmer, Jiang & Koch (JFE 2022, 'Overnight returns, daytime reversals, and future stock returns') document a pattern in these two legs. A daytime reversal of a NEGATIVE overnight return (NR day: overnight < 0, intraday > 0) shows informed daytime traders correcting overnight noise-trader selling. A daytime reversal of a POSITIVE overnight return (PR day: overnight > 0, intraday < 0) shows daytime traders leaning against overnight noise-trader buying. Stocks with a high frequency of NR days minus PR days over the past month earn higher returns over the next month, and stocks with low NR-minus-PR earn lower returns. The 'tug of war' is between overnight retail/sentiment flow and daytime institutional flow, and the daytime side is better informed. Universe, fixed ex ante (30 liquid, sector-diverse large caps, all with USEQ 1-DAY history from 1970-2012 verified in the catalog): JPM, AAPL, MSFT, AMZN, GOOGL, META, NVDA, BAC, XOM, CVX, JNJ, PFE, MRK, KO, PEP, PG, WMT, HD, MCD, DIS, INTC, CSCO, ORCL, IBM, T, VZ, CAT, BA, GE, UNH (all '.USEQ'). JPM is the primary/clock leg. A name enters the ranking only after it has lookback_days of its own history, so META (2012) and GOOGL (2004) join late. The score for stock i is TUG_i = (#NR days - #PR days) / lookback_days over the trailing lookback_days sessions. Every rebalance_sessions sessions, counted by bars rather than calendar dates, rank the eligible names by TUG. Go long the top k_per_side names and short the bottom k_per_side, equal dollar per name. The book is dollar-neutral, with 50% of equity gross per side and 100% gross total. The leverage multiplier stays at the config default; no leverage field is declared in the plan. This mechanism is new to the corpus. It is not price momentum (it is a sign-pattern frequency, not a return level), not short-term reversal, and not a calendar window: the trigger is an observed return pattern, and the rebalance is a bar-count cadence. Implementation notes for speed (two prior USEQ basket runs timed out): keep one bounded deque of +1/0/-1 day codes per name plus a running sum, so each bar is O(1). Rank only on rebalance bars, after all 30 legs have printed the same session (timestamp barrier), with a sort of 30 floats. Never rescan history. Inspiration: Akbas et al. 2022 JFE; Lou, Polk & Skouras 2019 JFE 'A tug of war: overnight versus intraday expected returns'.

A dollar-neutral, multi-instrument, long-short cross-sectional strategy on 15 OKX USDT perpetual swaps (OkxCollector USDT-swap OHLCV, catalog ids <BASE>USDT.OKX, 1-DAY bars, every leg verified in the catalog with daily history starting 2020-01 to 2020-12). Signal: the George & Hwang (2004) 52-week-high ratio PTH_i = close_i / max(high_i over the trailing N daily bars). The mechanism is anchoring rather than return momentum. Traders use the trailing high as a reference point and under-react to news that pushes a coin toward it, because resistance sellers and break-even sellers cluster there. Coins that are deep below their high carry overhang from trapped holders selling into every bounce. This is NOT a return-ranked momentum rotation: two coins with identical 30-day returns can have opposite PTH ranks. Every Monday 00:00 UTC daily close, rank the 15 names by PTH. Go long the top K and short the bottom K at equal dollar weight per leg, so the book is net-zero dollar. Names already in the right bucket are held, not churned. Data check per L186/L181: all 15 1-DAY bar dirs exist, with first bars from BTC/ETH/BCH/ETC 2020-01-02 to AAVE 2020-12-12, giving ~5.8 years of overlapping history. Implementation note to avoid sandbox_timeout: keep a fixed-length deque of highs per instrument, never rescan the full history per bar, and rank only on the rebalance bar.

Cross-venue, long-short, beta-hedged relative-value strategy. It pairs two US-listed crypto-exposed stocks (COIN.USEQ and HOOD.USEQ on 1-DAY bars from the Yahoo UsEquitiesCollector; COIN daily data starts 2021-04-14, HOOD starts 2021-07) with the Bybit BTC linear perpetual (BTCUSDT.BYBIT on 1-HOUR bars from the Bybit collector, starting 2020-03-25). Both legs were checked and are present in the catalog. At each session close, for each stock, compute its daily log return r_s and the BTC log return r_b over the SAME window. The BTC price comes from the latest BTCUSDT.BYBIT 1-HOUR bar whose close timestamp is at or before the equity bar's close (20:00 or 21:00 UTC). Find that bar with bisect on a sorted timestamp array built once, so each bar costs O(log n); never rescan history. Fit a rolling OLS beta of r_s on r_b over beta_lookback=60 sessions. The daily residual is e = r_s - beta*r_b. R5 is the 5-session sum of residuals, and z = R5 / stdev of 5-session residual sums over resid_vol_lookback=120 sessions. When |z| >= z_entry, fade the stock-specific move. If z >= +z_entry, short the stock and buy a beta-scaled BTC perp. If z <= -z_entry, buy the stock and short the beta-scaled BTC perp. The premise is short-horizon residual reversal (Lehmann 1990; Jegadeesh 1990), applied to a factor that equity residual models usually miss. Crypto-equity names trade on retail and ETF flows that overshoot the 24/7 crypto price that drives their fundamentals. BTC is the only correct hedge factor because it is the mechanism, which is why a BTC instrument is used despite BTC's corpus share. The BTC leg goes on BYBIT (2.3% of the corpus), not BINANCE (62%). This differs from the in-pipeline CoinOvernightBtcUnderreactionIntradayLS, which trades intraday catch-up toward BTC within one session on 1h bars; this strategy trades multi-session reversal of the residual, hedged, on daily bars. It also differs from the failed stock-vs-sector-ETF reversal, which died on a config/leverage mismatch rather than on economics, and used a different factor and universe. Sizing: the stock leg is stock_notional_pct (15%) of equity. The BTC leg's notional is beta times the stock notional, capped at 2x the stock notional. At most one open pair per stock, so at most 2 concurrent pairs and gross exposure of about 75% of equity or less; no leverage is needed. Leave config.leverage at its default and do not add a leverage parameter. Fee arithmetic: COIN's 5-session residual sigma is about 6-7% (stock-specific daily vol about 3%), so a 2-sigma trigger is a residual dislocation of about 13%. If about 25% of it reverts during the hold, the stock leg captures about 3.0% and the hedged pair returns about 1.2-1.5% on total two-leg notional. Round-trip cost is about 0.05% on the USEQ leg (spread plus impact) and about 0.11% on the Bybit leg (taker), roughly 0.10% blended on gross notional, so expected capture is more than 10x cost. Firing rate: a two-sided 2-sigma trigger on two names with holds of up to 10 sessions gives about 10-14 entries per name per year, about 100-130 pair trades over about 5.3 years. The trigger is a market observation, never a calendar date.

Two-instrument long-short pair on USEQ daily bars, OHLCV only. GDX starts in 2006 and GLD in 2004, so the overlap is about 19 years. GDX (gold miners) behaves like a levered claim on gold (GLD) because of the miners' operating leverage. Each session the strategy computes: (1) a rolling OLS beta of GDX daily log returns on GLD daily log returns, using data up to the previous session; (2) the daily residual; (3) the residual summed over the last resid_window sessions; and (4) a z-score of that sum against the prior 250 sessions. Large miner moves that gold does not explain are expected to partly reverse within weeks, because miner cash flows stay tied to the gold price. Examples are equity risk-off selling, ETF creation/redemption pressure and sector rotation. This design avoids the dead families in the lessons: the trigger is not a calendar date (L179), there is no crypto supplementary-feed gate (L111), and it uses no options (L102). Leverage is not a research parameter. Gross exposure comes from equity through the fixed gross_exposure_pct.

Test independent long and short failed-breakout trades on XLM and ALGO perpetuals using 4-HOUR OHLCV. A completed breakout that promptly closes back inside its prior range may trap breakout participants and induce an unwind toward the range interior. HyperliquidCollector supplies perpetual OHLCV for both exact instruments. Catalog filenames indicate overlapping coverage from 2024-06-21 through 2026-10-02; validate actual timestamps, gaps and duplicates before development. Implement as a FactoryStrategy subclass with bounded rolling buffers and execution after the confirming close. No calendar, funding, news or tick signal is required.

Test equal-dollar opposing ATOM perpetual positions on synchronized 4-hour bars, earning a persistent funding differential rather than predicting directional returns or small price-spread convergence. Primary: ATOMUSD.HYPERLIQUID; hedge: ATOMUSDT.BYBIT. Local parquet filenames indicate overlapping bar coverage from 2024-02-12 through 2026-10-01; row-level continuity and instrument-specific funding coverage must be audited before development. HyperliquidCollector and BybitCollector support the required perpetual bars and funding histories. Funding cadence must follow actual settlement timestamps, including interval changes. Official mechanism references: [Hyperliquid funding](https://hyperliquid.gitbook.io/hyperliquid-docs/trading/funding) and [Bybit funding history](https://bybit-exchange.github.io/docs/v5/market/history-fund-rate). This is an unmeasured hypothesis, not an established arbitrage.

Multi-instrument, long-short, beta-hedged event strategy on USEQ 1-DAY bars, pure OHLCV. The sector ETFs have daily history from 1998 and the stocks from earlier. Universe: 4 SPDR sector ETFs, each with 5 of its largest members: XLE {XOM, CVX, COP, EOG, SLB}, XLF {JPM, BAC, WFC, C, GS}, XLU {NEE, DUK, SO, D, AEP}, XLP {PG, KO, PEP, WMT, COST}. For each stock, the daily residual is e_t = r_stock - beta*r_etf, with beta from a rolling 120-session regression using data up to the prior session. The signal is z = sum(e, 5) / (sd(e,120) lagged 5 sessions * sqrt(5)). When |z| >= 2.5, take the opposite side of the stock, hedge with beta times the stock notional in its sector ETF, and hold 10 sessions. Mechanism: a liquidity-provision premium for absorbing stock-specific 1-week price pressure (fund flows, block trades, overreaction to news). This is the residual short-term reversal documented by Lehmann 1990, Lo-MacKinlay 1990 and Da-Liu-Schaumburg 2014. It is measured against the stock's own sector, because sector moves trend while within-sector residuals revert. Zero USEQ commissions make a ~0.3% per-event edge viable. PRE-STUDY on the in-repo catalog, 2008-2026, these exact 20 names and rule, at most one open event per name: 911 events (~49/yr). Mean 10-session residual capture +0.33% of stock notional, median +0.22%, hit rate 54.3%, per-event standard deviation 3.6%, t about 2.75. 13 of 19 years are positive; since 2020 the mean is +0.39% on 356 events. All 4 sectors are positive (XLF +0.64%, XLU +0.35%, XLP +0.20%, XLE +0.15%). In a broader 41-name check, fading up-moves (+0.34%) and fading down-moves (+0.33%) earned about the same. That check had 1865 events with a mean of +0.33%. XLE was kept despite being the weakest, to avoid choosing names on in-sample results. Why this slot: USEQ is 5.4% of the corpus, long-short is 16%, and Binance/BTC is over-concentrated. The trigger is a price observation, not a calendar date (L179). It is not a crypto perp trade gated on funding or open interest (L111). Unlike the abandoned dual-class OU spread and the in-pipeline duopoly pairs, there is no cointegration fit: the hedge is the stock's own sector ETF, and a high threshold selects only large residual moves. IMPLEMENTATION NOTE: two earlier USEQ multi-name runs hit the 300-second sandbox timeout, so every statistic must update in O(1) per bar. Keep a fixed-length buffer of residuals per name, running sums of x, y, x^2 and xy for the 120-session beta, a running 5-session residual sum, and a running sum and sum of squares for the 120-session standard deviation. Never rebuild lists, call pandas or rescan history inside on_bar or on_extra_bar. Process each date only after all 24 bars for it have arrived.

Test whether SAND adjusts with a delay after concordant, substantial 24-hour moves in GALA and APE. Trade SAND long or short using synchronized 4-HOUR bars; GALA and APE are signal sources only. HyperliquidCollector supplies perpetual OHLCV for all three exact instruments. Catalog inspection verified 4,975 common timestamps from 2024-06-21T04:00:00Z through 2026-10-01T20:00:00Z, with close-stamping markers present. A baseline trigger-frequency check found 93 non-overlapping signals, approximately 41/year; it did not inspect subsequent returns. Implement as a FactoryStrategy subclass with bounded rolling buffers and timestamp-matched extra-bar handling. Statistical inspiration: https://quant.stackexchange.com/questions/81139/lead-lag-statistical-test-in-python ; this source suggests testing lead–lag relationships and supplies no evidence of profitability for these tokens.

Test whether relative resilience during a joint DeFi selloff predicts further divergence: long the more resilient token and short the weaker token, using synchronized 4-HOUR bars on AAVEUSD.HYPERLIQUID and UNIUSD.HYPERLIQUID. Hold for up to three days. HyperliquidCollector supplies perpetual OHLCV; direct parquet inspection verified 5,879 shared timestamps from 2024-01-25T20:00:00Z through 2026-10-01T20:00:00Z, with two discontinuous intervals. Reset indicators after gaps. Inspiration: https://bigquant.com/wiki/doc/BDR9U0WPw2 discusses measuring resilience when another asset falls; it does not establish this proposed crypto trading edge. No funding income, options data, or calendar trigger is assumed.

Test whether temporary disagreement between AAVE and UNI reverses after a shared sector move: buy the relative laggard and short the relative leader using synchronized 4-HOUR bars. This is an event-driven, equal-dollar pair, not a claim that their price levels are cointegrated. HyperliquidCollector supplies perpetual OHLCV. Exact local catalog inspection verified 5,879 shared close timestamps from 2024-01-25 through 2026-10-01, with two missing grid timestamps within that span. Signals use completed bars only; orders execute after signal formation. Expected capture and event frequency below are preregistered predictions, not measured results.

Test whether unusually large relative moves between two established proof-of-work assets partially reverse after observable exhaustion. Trade equal-dollar, opposing LTC and BCH perpetual positions using synchronized 4-HOUR bars. HyperliquidCollector supplies both exact perpetual instruments. Local stamped catalog coverage overlaps from 2024-01-25T16:00:00Z through 2026-10-01T20:00:00Z; validate continuity before execution. This is a falsifiable relative-liquidity hypothesis, not an assumption of permanent cointegration. No funding income is included.

On synchronized 4-hour bars, buy PURRUSDC.HYPERLIQUID_SPOT and short equal notional PURRUSD.HYPERLIQUID when the perpetual premium exceeds 1.2%. The catalog contains 4,172 overlapping bars; 69 separate episodes crossed that threshold before liquidity filters. Include actual hourly funding payments. Hyperliquid funding mechanics: https://hyperliquid.gitbook.io/hyperliquid-docs/trading/funding

A cross-sectional low-volatility factor on OKX USDT perpetual swaps. Data comes from OkxCollector as 1-DAY OHLCV bars. In the catalog, every leg has data from 2020 or 2021 through 2026-10-01. The universe is 13 coins, each with at least 2077 daily bars: ADA, DOT, LINK, ATOM, ETC, BCH, XLM, TRX, AAVE, UNI, ALGO, SOL and AVAX. XRP, DOGE, LTC, NEAR and FIL are left out on purpose because OKX has only 8 days of history for them. Every rebalance_days bars, rank the coins by realized vol over vol_lookback days. Go long the n_per_side lowest-vol coins and short the n_per_side highest-vol coins. Each leg is sized by inverse volatility, and the two books are matched to equal ex-ante volatility, which makes the portfolio roughly beta-neutral. Total gross exposure is 1.0x equity. Rolling vol must be computed in O(1) per bar (deque with running sums), and ranks only on rebalance bars, to avoid the recent sandbox_timeout deaths.

Trade EWYUSDT against SAMSUNGUSDT and SKHYNIXUSDT using synchronized 1-hour Bybit perpetual bars. Adapt the economically linked three-leg residual described at https://www.fmz.com/digest-topic/10996. The local catalog has 2,868 overlapping hourly bars from 2026-06-04 through 2026-10-01. Estimate the relationship using only prior bars; enter only when the deviation is large enough to clear costs.

Faithful research translation of jswapnil’s TradingView Pine v6 strategy on DOGEUSDT.BINANCE_SPOT 5-minute bars. The source uses a fixed 100,000-unit forex lot, $10,000 initial capital, 500:1 margin and zero commission. Those account settings cannot be used on this platform’s $100,000 spot CASH account, so each trade is capped at 20% of current equity with 1x leverage. All signal and exit defaults remain fixed. The engine charges Binance spot taker fees of 0.10% per side; the source’s separate 0.6-pip exit adjustment is also retained. Source performance claims are unverified.

Translate the supplied Pine strategy on BTCUSDT.BINANCE 4-hour bars, matching the author's published backtest interval. The author separately suggests 1-hour bars; that is not the published backtest setting. Evaluate signals at bar close and submit orders afterward. Preserve the source's indicator calculations and defaults listed below. Its date-range condition is commented out, so it must not restrict trading. Display settings and computed SSL1, SSL2, ATR bands, QQE trend counters, and the first QQE threshold do not affect orders. The published performance is unverified.

A source replication of ianzeng123's FMZ strategy 504549 on ETHUSDT.BINANCE daily bars. The supplied Pine code enters long and short positions using a candle-entropy score. Its default order size is a fixed 10 ETH; the author's comment about selecting 100% of equity in Properties does not change that code default. Performance claims remain unverified. Fixed sizing conflicts with this factory's capital-relative sizing convention and must be recorded as a fidelity exception.

Translate the supplied Pine v6 strategy by kamil_vaslavsky1 onto 30-minute BTCUSDC.BINANCE USD-M perpetual bars. It is a tradeable long/short strategy, but its claimed performance is unverified. Preserve the source calculations, defaults, close-only decisions, flat-only entries, and explicit ATR-based quantity. The source specifies $10,000 initial capital, a 100%-of-equity default quantity, 0.1% commission per fill, two ticks of slippage, calc_on_every_tick=false, process_orders_on_close=true, and max_bars_back=5000. Its explicit entry quantity overrides the default quantity. The platform instead starts with $100,000 and applies its Binance USD-M fee model: 0.05% taker per fill, approximately 0.10% round trip. Model the source's two-tick slippage separately where execution configuration permits. Confirm that the exact BTCUSDC.BINANCE contract and required bar span exist before coding; do not silently substitute BTCUSDT.

Faithful hypothesis for ianzeng123's Gold Multi-Confluence Trading System. Despite its title, the source backtest specifies BTC_USDT Binance futures on 1-hour bars. Evaluate the supplied Pine rules on BTCUSDT.BINANCE with one long or short position at a time. The source's performance claims are unverified. Preserve its actual code behavior: volume and session each contribute one score point rather than acting as mandatory filters; the declared spread filter is never applied; and the Bollinger squeeze condition is always false because it compares the current width with a minimum that includes the current width.

A default-parameter translation of gvman99’s tradable Pine strategy on 5-minute SOLUSDT.OKX perpetual bars. Preserve its two-unit entry, one-unit trim, runner, no pyramiding, bar-close signals, and intrabar limit and stop orders. On this platform, two units mean equal tranches sized from current equity, capped at 20% total notional and 1% initial equity risk. Source performance is unverified.

Source: mpeac, 'Why aren't the optimal entry/exit thresholds for OU pairs trading relatively invariant to shifts in the OU mean?', quant.stackexchange.com/questions/72234 (2022-09-18), which discusses Leung & Li, 'Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit'. Classification: (a). The post is a theory question, but it names a concrete mechanism: trade a mean-reverting OU spread, entering at a cost-aware threshold below/above the mean and exiting at an optimal level, with a stop. Answer to the question, which shapes the design: Leung-Li's thresholds are not translation-invariant because their problem is long-only optimal stopping on the discounted LEVEL of the spread (the reward is e^{-rt}(X_t - c)). Discounting a level that changes sign makes the value depend on where theta sits relative to 0. Theirs is not a symmetric long/short problem on the deviation X - theta. For a two-sided spread trader with negligible carry, the right formulation is in deviation units: entry and exit bands are set relative to a point-in-time theta and are symmetric for both sides. That is the invariance the asker expected. We use that formulation. The open question is which spread really is OU. Statistical pairs are dead in this factory (L113, the mean_reversion tag is 0/203), so we use the one spread class where mean reversion is contractual: two share classes of the SAME company. They have identical cash flows and differ only in voting rights. The log price ratio is pinned to a slowly drifting voting premium (theta), and liquidity and index-flow shocks push it away from that premium. Fit OU on log(A/B) over a rolling window, trade z-deviations from the rolling theta, and hold for days. Dollar-neutral: long the cheap class, short the rich class, same notional. All three pairs run in one strategy instance, each as its own independent sleeve. USEQ is under-represented at 5.0% of experiments, and long_short is under-represented at 15.7%. PRE-STUDY (point-in-time rolling mean and std, entry |z|>k, exit at z crossing 0 or a 20-session cap, daily closes from the catalog via load_normalised). Gross return per trade is in % of total two-leg notional. GOOGL/GOOG since 2014-04: L60/k2.0 gives 83 trades, gross +0.12%, win 77%, 12 of 13 years positive. L20/k2.0 gives 138 trades, +0.12%, win 85%, 13 of 13 years positive. NWSA/NWS since 2013-07: L60/k1.5 gives 126 trades, +0.25%, median +0.43%, win 72%. L120/k2.0 gives 57 trades, +0.26%. NWS has 4 losing years: 2013, 2021, 2023 and 2024. FOXA/FOX since 2019-03 is the weakest: L20/k2.0 gives 88 trades, +0.10%, win 76%, and the longer windows are about 0. FOX is kept as a third, independent sleeve so that the hypothesis can be checked against it rather than tuned to it.