The Realized Jumps Predict Cryptocurrency Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for The Realized Jumps Predict Cryptocurrency Returns 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: The Realized Jumps Predict Cryptocurrency Returns
# 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
Good Volatility, Bad Volatility, and the Cross Section of Cryptocurrency Returns
Zehua Zhang; Ran Zhao
- Hunan University
- Claremont Graduate University
- ?Claremont Graduate University, Drucker School of Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3910202
Strategy in a nutshell
The investment universe consists of the 51 most actively traded cryptocurrencies with market capitalization above $1 million, obtained from FirstRateData (filtered to be U.S. dollar–based). Realized volatility is computed over 5-minute intervals. First, calculate the daily realized volatility as the sum of squared returns, and the positive and negative semivariances as the sum of squared returns multiplied by an indicator for positive or negative returns. Next, compute the signed jump measure as the difference between positive and negative semivariances. The realized signed jump measure (RSJ) is then defined as the signed jump divided by realized volatility. Each day, cryptocurrencies are sorted into quintiles based on RSJ. The strategy goes long the bottom quintile and short the top quintile. Portfolios are rebalanced daily and equally weighted.
Economic rationale
Volatility captures both positive (“good”) and negative (“bad”) variations in returns. Research shows that positive semivariance is more informative for predicting future realized volatility than negative semivariance. Higher positive semivariance or signed jump variation tends to lead to higher future realized volatility, while higher negative semivariance and minor signed jumps predict lower future volatility. Betting on greater “good” volatility and signed jumps can appear attractive, as one expects these jumps to repeat, but this often carries higher uncertainty and little fundamental support. In the crypto market, extrapolating past signed jump behavior to predict future performance follows a similar pattern as traditional momentum, yet realized signed jumps generally act as a negative predictor of future returns.