Reversal in Small Cryptos
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Reversal in Small Cryptos in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Reversal in Small Cryptos
# Detected pattern: Mean reversion
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Buy when return z-score < -1 over 20 days.
def Rebalance(self):
import numpy as np
picks = []
for symbol in self.symbols:
hist = self.History(symbol, 20 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
rets = close.pct_change().dropna()
if len(rets) < 20: continue
window = rets.iloc[-20:]
z = (window.iloc[-1] - window.mean()) / (window.std() or 1e-9)
if z < -1:
picks.append(symbol)
w = 1.0 / len(picks) if picks else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w if symbol in picks else 0.0)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
Value Premium, Network Adoption, and Factor Pricing of Crypto Assets
Lin William Cong; George Andrew Karolyi; Ke Tang; Weiyi Zhao
- Cornell University
- ?Cornell University - Samuel Curtis Johnson Graduate School of Management
- Tsinghua University
- ?Institute of Economics, School of Social Sciences, Tsinghua University
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3985631
Strategy in a nutshell
The investment universe consists of cryptocurrencies from CoinMarketCap.com. Stablecoins, and coins with zero price, market capitalization, or trading volume across all periods are excluded. Non-return variables are winsorized at the 1st and 99th percentiles. Market capitalization and prices are sourced from CoinMarketCap.
First, cryptocurrencies are sorted into five portfolios based on their week-end market capitalization. Within each size quintile, coins are then sorted into five portfolios according to their past two-week returns (momentum). Among the three smallest size portfolios, the strategy goes long the bottom momentum portfolios. This variant is used and averaged to ensure the strategy is executable and avoids relying on the very smallest cryptos. The momentum effect is strong in smaller to mid-sized cryptos but reverses in the largest size quintile.
The three lowest-momentum portfolios in the small size quintiles are equally weighted, while each portfolio is value-weighted individually. The strategy is rebalanced weekly.
Economic rationale
The underlying economic reason for the strategy is not explicitly defined. The effect was identified using a portfolio-sorting approach and is highly statistically significant, but the paper does not provide a precise behavioral explanation. Possible drivers include mean-reversion or prior underappreciation of certain cryptocurrencies before the signal formation.
Robustness tests across different lookback periods—one, three, or four weeks—show qualitatively similar results, confirming that the effect is not sensitive to the exact two-week window and remains statistically significant.