KarapticResearch / Index
A public quantitative trading research log testing published crypto trading strategies against real Binance data. Literature review → replication → gross → net-of-costs → verdict, one note per hypothesis. Closed and negative results are published alongside positive ones.
Open a note for the full, indexable abstract and links to methodology, code, data, and references.
Introduces the series and its method. An audit of roughly 200 pre-built trading strategies on a marketplace (a "CMC Skill Hub") found that most rest on the same public metrics anyone can compute for free.
Read note ↗Asks whether grid trading produces an independent trading edge, or whether its returns come from market exposure, execution economics, and market-making infrastructure. Chen, Chen & Jang derive analytically that, under a symmetric random walk, a classical grid's expected profit is zero before costs.
Read note ↗Tests whether a cross-sectional momentum spread survives on Binance USDⓈ-M perpetuals net of realistic costs, not just in an academic backtest. Liu, Tsyvinski & Wu document momentum as statistically significant across a broad crypto sample; Starkiller Capital's practitioner backtest beat the market in relative terms through the 2021–2022 bear market without an absolute profit, and named a 125-basis-point cost threshold that kills the effect.
Read note ↗Asks whether time-series momentum (well established on traditional assets by Moskowitz, Ooi & Pedersen) still holds in crypto today, or whether early favorable evidence (Liu & Tsyvinski, pre-2018 data) described a regime that has since passed. Grobys, Kolari, Sandretto, Shahzad & Äijö's 2025 study, the longest and most recent available (2016–2023, 30 large-cap coins), splits the sample by regime: strong pre-July-2020, but negative and not statistically significant in the post-July-2020 period that matters for a trader today.
Read note ↗Perpetual funding carry arrives with strong academic backing: He, Manela, Ross & von Wachter report a Sharpe of 1. 80 for a spot-short-perpetual carry trade, and Schmeling, Schrimpf & Todorov document crypto carry averaging roughly 7%/year on a fixed-date basis.
Read note ↗The literature contains two unrelated strategies both called "reversal." Branch A (Dobrynskaya): buy coins that just crashed, hold 10–12 weeks, a bubble-and-burst pattern found on a roughly 2,000-coin CoinMarketCap sample, 2014–2020.
Read note ↗Unlike the rest of the series, this note goes system-first: Klines, an event detector built from months of manual replay of Binance volume spikes, is checked against the academic literature only after it had a stable shape: eleven papers covering why volume anomalies predict returns, how exits should be managed (triple-barrier, maximum adverse excursion), and what remains genuinely open. On the larger, pre-patch walk-forward sample, the core "ALIVE" tier shows an 85% win rate and the narrower "ROCKET" tier 100%, stable across eleven weekly cohorts; a newer, more honestly timed log (real detection timestamps instead of a hardcoded offset) shows a materially weaker 63.
Read note ↗BOS is a standalone structural-scanning lab, built alongside Klines rather than inside it. It detects confirmed swings, breaks of structure, and persistent impulses causally across 1H, 4H, and 1D BTC data.
Read note ↗Audits 19 tools in Binance Skills Hub to determine whether any provides a reproducible, execution-ready strategy for Binance Spot or Futures. Static inspection and two live laboratories found one useful CEX access layer for order books, trades, candles, derivatives data, and account operations, but its trading logic must be supplied by the user.
Read note ↗Full repository, methodology and references: github.com/Silent47boryara/searching-for-edge ↗