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Crypto · Global · cryptos · Weekly · Global Free

Price-based Value in Cryptocurrencies

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Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Price-based Value in Cryptocurrencies 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 →

Ready — edit code, then Run backtest.
IDE · 50 lines
Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports
Only these libraries are allowed in the sandbox.
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_metrics
ASSETS list (whitelisted ETFs)
Module-level list. Tickers must be in the Quant Buffet whitelist.
ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]
make_on_day contract
Must return (on_day, ready). on_day calls engine.set_target_weights.
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, ready
Set target weights
Weights should sum to about 1.0. Empty dict = 100% cash.
engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR
37.74%
Sharpe
0.89
Max DD
-78.46%
Vol
49.79%
Sortino
1.42
Beta
0.22
Up days
40%

Run the backtest to populate charts.

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.

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Absolute momentumAssets: SPY, TLT, GLD, BIL
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Price-based Value in Cryptocurrencies
# Detected pattern: Absolute momentum
# 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: Long assets with positive 252-day return; equal-weight; monthly.

    def Rebalance(self):
        # Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
        # Default: equal-weight. Port your make_on_day weights here via SetHoldings.
        w = 1.0 / len(self.symbols) if self.symbols else 0.0
        for symbol in self.symbols:
            self.SetHoldings(symbol, w)

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

AuthorsLin William Cong; George Andrew Karolyi; Ke Tang; Weiyi Zhao

Institute
  • Cornell University
  • ?Cornell University - Samuel Curtis Johnson Graduate School of Management
  • Tsinghua University
  • ?Institute of Economics, School of Social Sciences, Tsinghua University

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3985631

Screenshot from the original paper

Screenshot from the original paper
Screenshot from the original paper

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. The value measure is defined as the negative of the past 52-week return. Cryptocurrencies are sorted into decile portfolios based on this value measure. The strategy goes long the top decile (most undervalued) and short the bottom decile (most overvalued). Portfolios are rebalanced weekly and weighted by market value.

Economic rationale

Traditional equity-based value metrics, such as cash flows or book-to-market ratios, are not applicable to cryptocurrencies. Instead, relative value is estimated using the definition from Asness, Moskowitz, and Pedersen (2013), where value in hard-to-value asset classes is the negative of long-term returns. This approach identifies relatively cheap and expensive assets, under the assumption that undervalued cryptocurrencies will outperform overvalued ones over time.

Backtest performance

Annualised return37.74%
Volatility49.79%
Beta0.22
Sharpe ratio0.89
Sortino ratio1.42
Maximum drawdown-78.46%
Win rate40%