Geopolitical Risk and the Cross-Section of Cryptocurrency Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Geopolitical Risk and the Cross-Section of 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: Geopolitical Risk and the Cross-Section of 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
Is Geopolitical Risk Priced in the Cross-Section of Cryptocurrency Returns?
Huaigang Long; Ender Demir; Barbara Będowska-Sójka; Adam Zaremba; Syed Jawad Hussain Shahzad
- Zhejiang University
- TRIstanbul Medeniyet University
- Poznań University of Economics and Business
- Montpellier Business School
- ?Poznan University of Economics and Business
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4109293
Strategy in a nutshell
The investment universe consists of all cryptocurrencies with daily price, volume, and capitalization data available on https://coinmarketcap.com/. Assets with a market cap of less than 1 million dollars and those with a trading history shorter than 60 days are excluded.
To proxy for geopolitical risk, the GPR index is constructed following Caldara and Iacoviello (2022). It is based on calculating the frequency of geopolitical event-related articles in major newspapers.
Now geopolitical beta is calculated using a rolling time-series regression of excess daily returns on a daily change in GPR and the following control variables: excess returns on the market, size, and momentum factors. The equation can be found on page 4 of the paper. The estimation period is 21 days, but it is robust to adjustments.
Sort the cryptocurrencies into value-weighted quintiles according to their geopolitical beta. Long the lowest geopolitical beta quintile, short the highest. Rebalance weekly.
Economic rationale
The GPR index, formerly constructed by Caldara & Iacoviello, is a measure of the geopolitical risk in the world. By approximating the geopolitical beta based on this index for a given cryptocurrency, its sensitivity to geopolitical events is measured. The results support a hypothesis that investors are likely to be willing to pay a premium for assets with low geopolitical beta. Therefore, price and geopolitical beta are negatively correlated, which is the base idea of this strategy.