Conditional FX Correlation Risk
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Conditional FX Correlation Risk 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: Conditional FX Correlation Risk
# Detected pattern: Momentum rotation
# 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: Hold top 1 by 126-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 126 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 126 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:1]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
Dynamic Allocations for Currency Investment Strategies
Kei Nakagawa; Ryuta Sakemoto
- JPNomura Holdings (Japan)
- ?Nomura Asset Mamagement Co,Ltd
- JPOkayama University
- JPKeio University
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4073980
Strategy in a nutshell
The dataset consists of daily spot and one-month forward exchange rates sourced from Datastream, analyzed from the perspective of a U.S. investor with the U.S. dollar as the base currency. Conditional correlations between FX spot rate changes are estimated over rolling three-month windows across nine FX pairs, generating 36 correlation measures.
At each month-end, the correlations are ranked into deciles, and the cross-sectional dispersion of conditional FX correlations (FXC) is calculated as the difference between the top and bottom deciles. The innovation in FXC (ΔFXC) is then extracted. Currency pairs are sorted based on their factor betas with respect to ΔFXC, and three portfolios are constructed: long in low-beta currencies, short in high-beta currencies, with the intermediate group excluded.
Currency excess returns are measured using forward premiums: for long positions, as the difference between the bid price of the one-month forward and the spot ask price (scaled by the ask); and for short positions, as the difference between the spot bid and the one-month forward ask (scaled by the bid). The total portfolio excess return equals the sum of long and short position excess returns.
Economic rationale
The strategy builds on Mueller et al. (2017), who show that FX correlations become more dispersed during periods of financial stress: high-correlation pairs become even more correlated, while low-correlation pairs diverge. This widening of the cross-section creates priced risk exposure.
Currencies that serve as hedges in stressful periods (high-beta pairs with respect to ΔFXC) deliver lower average returns in normal times, while currencies that perform poorly in stress (low-beta pairs) deliver higher average returns. The negative relation between ΔFXC betas and excess returns supports the presence of a priced FX correlation risk factor. Thus, the strategy profits from systematically exploiting this risk premium embedded in cross-sectional FX correlation dynamics.