Cryptocurrency momentum effect: DFA and MF-DFA analysis
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Cryptocurrency momentum effect: DFA and MF-DFA analysis 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
Accent = strategy · dashed grey = buy-and-hold benchmark
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: Cryptocurrency momentum effect: DFA and MF-DFA analysis
# 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
Cryptocurrency momentum effect: DFA and MF-DFA analysis
Qing Cheng; Xinyuan Liu; Xiaowu Zhu
https://www.semanticscholar.org/paper/a3191ef73ab48efbb7bd887620eaea52c07f94fd
Teaser
Hold assets with positive trailing return; equal-weight the winners. Universe: BTC-USD, ETH-USD. Parameters: lookback=252; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
Abstract Cryptocurrency has experienced the skyrocketing and falling back in 2018. Beyond the hype, the specific price movements of different cryptocurrencies should be investigated in a more careful way. Since the cryptocurrency market is a non-linear complex system which are not suitable analyzed by tradition methods, this paper introduces methods from econophysics. Mono-fractal analysis (detrended fluctuation analysis, DFA) is applied to investigate the price movement. Further, multi-fractal fluctuation detrended analysis (MF-DFA) is used for robustness test. Through analyzing four representative cryptocurrencies, our paper finds a strong momentum effect in BTC and ETH market, and a reversion effect in XRP and EOS when large fluctuation occurs. These findings may provide a reference for
Economic rationale
Positive time-series momentum indicates a favorable trend state; holding only assets with positive trailing returns tilts toward that premium. Related evidence from “Cryptocurrency momentum effect: DFA and MF-DFA analysis”: Abstract Cryptocurrency has experienced the skyrocketing and falling back in 2018. Beyond the hype, the specific price movements of different cryptocurrencies should be investigated in a more careful way. Since the cryptocurrency market is a non-linear complex system which are not suitable analyzed by tradition methods, this paper introduces methods from econophysics. Mono-fractal analysis (detrended fluctuation analysis, DFA) is applied to investigate the price movement. Further, multi-fractal