Building a Low-Frequency Quantitative FX Strategy Using Python(USD/JPY &EUR/JPY)

Building a Low-Frequency Quantitative FX Strategy Using Python(USD/JPY &EUR/JPY)

Building a Low-Frequency Quantitative FX Strategy Using Python(USD/JPY &EUR/JPY)



Here’s A Complete Guide with EUR/JPY & USD/JPY

Introduction

Low-frequency quantitative trading in foreign exchange (FX) markets sits at the intersection of macroeconomics, technical structure, market microstructure, and statistical pattern recognition.

Unlike high-frequency or ultra-fast execution models, low-frequency FX strategies rely on slower-moving macro drivers, regime shifts, volatility cycles, and technical confirmation.

In this article, we design a complete and production-ready Python framework for a low-frequency trading strategy focused on EUR/JPY and USD/JPY — two of the most structurally unique currency pairs in the global financial system.

We will explore:

  • Why JPY-based crosses behave structurally differently
  • The economic and liquidity mechanisms driving both pairs
  • The core indicators behind a robust low-frequency model
  • How to combine carry, volatility regimes, and price structure
  • A complete Python architecture (data, signals, backtest, execution)

This approach is designed for weekly or daily bars with position holding periods of 2–20 days, suitable for swing-level macro trading rather than intraday noise.

Here are all the key Charts & How they work:

Chart 1— USD/JPY: Price with SMA20 & SMA50

Title: USD/JPY — Price Trend Structure with SMA20 and SMA50

What This Chart Shows:

The same trend identification framework is applied to USD/JPY. The 20-day versus 50-day SMA crossover captures the broader structural moves driven by U.S.–Japan interest-rate spreads, BoJ interventions, and macro-policy shifts.

Why It Matters:

USD/JPY is even more sensitive to monetary-policy divergence than EUR/JPY. Trend filters are essential to avoid choppy periods around BoJ announcements and to align with long-lasting directional moves that often occur after major policy decisions (YCC adjustments, rate-hike cycles, etc.).

Chart 2— EUR/JPY Normalized ATR (Volatility Regime Filter)

Title: EUR/JPY — Normalized ATR (Volatility Regime Filter)

What This Chart Shows:

The chart displays ATR normalized by price (ATR_norm). This represents the percentage volatility of the pair. When ATR_norm spikes significantly above its long-term mean, it signals that the market has entered a high-volatility regime.

Why It Matters:

JPY crosses experience sharp volatility expansions during macro shocks — BoJ announcements, CPI releases, geopolitical tensions, or equity-market stress events. High-volatility regimes increase the probability of false breakouts and unpredictable price swings.

The strategy uses ATR_norm to:

  • reduce position sizes,
  • delay new entries, or
  • switch to a defensive state.

This mechanism stabilizes a low-frequency strategy and protects it from sudden JPY whipsaws.

Chart 3— EUR/JPY MACD Momentum Structure

Title: EUR/JPY — MACD Momentum and Histogram

What This Chart Shows:

The chart plots the MACD line (EMA12 — EMA26), its signal line (EMA9), and the MACD histogram. The histogram captures the momentum acceleration or deceleration in the pair.

Why It Matters:

Trend-following alone is insufficient, especially in FX where fake breakouts are common. The MACD histogram confirms whether a crossover is supported by momentum strength.

In this strategy:

  • Histogram > 0 → bullish momentum confirmation
  • Histogram < 0 → bearish momentum confirmation

Combining trend with momentum dramatically reduces false entries and aligns trades only with strong directional impulses.

Chart4 — EUR/JPY: Price with SMA20 & SMA50

Title: EUR/JPY — Price Trend Structure with SMA20 and SMA50

What This Chart Shows:

This chart illustrates how medium-term trend regimes can be identified using two simple moving averages: the 20-day and 50-day SMA. When the 20-day SMA rises above the 50-day SMA, EUR/JPY enters a bullish trend regime. Conversely, when the 20-day SMA falls below the 50-day SMA, the pair transitions into a bearish regime.

Why It Matters:

EUR/JPY tends to develop clean, sustained medium-term trends due to strong macro drivers such as ECB-BoJ policy divergence and changes in global risk appetite. The SMA20/50 crossover provides a robust low-frequency trend filter that avoids excessive noise and aligns the strategy with the major directional bias.

Chart 5 — EUR/JPY Composite Signal Score

Title: EUR/JPY — Composite Signal Score (Trend + Momentum + Carry + Volatility)

What This Chart Shows:

This chart illustrates the multi-factor signal score used for position decisions. The score integrates:

  • Trend regime (SMA20/50)
  • Momentum regime (MACD histogram)
  • Carry bias (interest-rate differential)
  • Volatility regime (ATR_norm)

A threshold-based rule converts the score into actual trading positions:

  • Score > 0.2 → long
  • Score < –0.2 → short
  • Otherwise → neutral

Why It Matters:

The signal-score approach ensures that positions are taken only when several independent factors agree. This dramatically lowers the turnover and makes the strategy genuinely low-frequency, with high signal quality.

Chart 6 — Strategy Portfolio Equity Curve (EUR/JPY + USD/JPY)

Title: JPY Strategy Portfolio Equity Curve (EUR/JPY + USD/JPY)

What This Chart Shows:

The final chart displays the combined equity curve of the strategy when applied equally to EUR/JPY and USD/JPY. The two pairs have different macro sensitivities:

  • EUR/JPY reacts strongly to global risk cycles.
  • USD/JPY is dominated by interest-rate and policy divergence.

By combining them, the portfolio benefits from diversification.

Why It Matters:

A dual-pair structure:

  • smooths the equity curve,
  • reduces drawdowns,
  • mitigates pair-specific shocks (especially BoJ-driven USD/JPY moves),
  • increases long-run Sharpe ratio.

This final chart is what transforms the model from a single-pair toy system into a professionally structured FX macro-quant strategy.


1. Why EUR/JPY & USD/JPY Behave Differently from Other FX Pairs

Before writing code, a quant trader must understand why these JPY pairs behave the way they do. Trading yen crosses without understanding the structural forces is like driving blindfolded.

1.1 JPY as a Funding Currency

Japan has:

  • structurally low inflation
  • near-zero or negative interest rates
  • a massive institutional investor base (GPIF, insurers)
  • persistent capital outflows
  • a domestic preference for global carry trades

This makes JPY the global funding currency, similar to CHF but with far deeper liquidity.

1.2 Resulting Behaviour

  1. JPY strengthens during risk-off
  2. Equities fall → vols rise → carry trades unwind → yen strengthens.
  3. JPY weakens during risk-on
  4. Global yield-seeking flows push EUR/JPY & USD/JPY upward.
  5. Volatility clustering is highly regime-dependent
  6. BOJ policy changes trigger trend breaks, not smooth transitions.
  7. Pairs respond differently:
  • USD/JPY = US rate expectations + UST yields
  • EUR/JPY = EU macro conditions + risk sentiment + cross-asset positioning

Thus EUR/JPY is more correlated with global equity sentiment,

while USD/JPY is more correlated with US 2Y yields.

A good quant strategy must integrate these behavioural traits.

2. Core Components of a Low-Frequency JPY Trading Model

We will build a model in four layers:

L1 Technical Regime Detection

  • SMA 20/50 crossing
  • ATR (volatility normalization)
  • Market structure (higher highs / lower lows)

L2 Macro / Carry Signals

  • Interest rate differentials
  • Yield curve slope
  • BOJ vs ECB / Fed policy divergence
  • FX carry attractiveness

L3 Volatility & Risk Regimes

  • VIX
  • MOVE index (for rates vol)
  • JPY specific implied vols (optional)

L4 Confirmation Indicators

  • RSI / CCI for exhaustion
  • Bollinger z-score for breakout
  • MACD histogram slope for momentum confirmation


The strategy should NOT trade every day.

It should wait for:

  1. Trend alignment
  2. Carry direction consistent
  3. Vol regime not hostile
  4. Momentum confirmation
  5. Risk management thresholds met

3. Strategy Logic Overview

3.1 Trend Detection (Technical Layer)

Use:

  • SMA20 > SMA50 → Bull regime
  • SMA20 < SMA50 → Bear regime

3.2 Volatility Adjusted Filters

ATR helps scale position size relative to pair volatility.

ATR_norm = ATR / Close

Rules:

  • If ATR_norm > threshold → avoid entries
  • If ATR_norm < threshold → normal mode

Because high ATR in JPY pairs usually signals BOJ / CPI shock.

3.3 Macro Carry Direction


We want to align trades with yield differential.

For EUR/JPY:

Signal_carry = (EU 2Y Yield - JP 2Y Yield)

If positive → long bias

If negative → short bias

USD/JPY equivalent:

Signal_carry = (US 2Y Yield - JP 2Y Yield)

3.4 Volatility Regimes (Risk-On vs Risk-Off)


JPY = risk-off hedge.

Define:

  • VIX < 18 → risk-on
  • VIX > 20 → risk-off
  • VIX > 25 → yen strength very likely

We only open long EUR/JPY or USD/JPY trades during risk-on.

We avoid long-yen carry unwinds during risk-off.

3.5 Entry Logic

Bull regime + carry positive + VIX < 18 + MACD histogram rising → Long

Bear regime + carry negative + VIX > 20 + MACD histogram dropping → Short

3.6 Exit Logic

  • Close when SMA20 crosses opposite
  • Or RSI hits overbought/oversold
  • Or ATR spike breaks volatility threshold
  • Or target/stop reached (ATR-based)

4. Python Implementation (Full Architecture)

Below is a full Python system outline.

(You can expand it into full backtesting engine later.)

4.1 Import Libraries

import pandas as pd
import numpy as np
import yfinance as yf
import talib as ta

4.2 Download Data

pairs = ["EURJPY=X", "USDJPY=X"]
data = {p: yf.download(p, start="2010-01-01") for p in pairs}
eurjpy = data["EURJPY=X"]
usdjpy = data["USDJPY=X"]

4.3 Indicators

def add_indicators(df):
    df["SMA20"] = ta.SMA(df["Close"], 20)
    df["SMA50"] = ta.SMA(df["Close"], 50)
    df["ATR"] = ta.ATR(df["High"], df["Low"], df["Close"], 14)
    df["ATR_norm"] = df["ATR"] / df["Close"]
    df["MACD"], df["MACD_signal"], df["MACD_hist"] = ta.MACD(df["Close"])
    df["RSI"] = ta.RSI(df["Close"], 14)
    df["Upper"], df["Middle"], df["Lower"] = ta.BBANDS(df["Close"], timeperiod=20)
    return df

Apply indicators:

eurjpy = add_indicators(eurjpy)
usdjpy = add_indicators(usdjpy)

4.4 Regime Filters

def regime(df):
    df["trend"] = np.where(df["SMA20"] > df["SMA50"], 1, -1)
    df["vol_regime"] = np.where(df["ATR_norm"] > df["ATR_norm"].rolling(100).mean() * 1.5,
                                -1, 1)
    df["momentum"] = np.where(df["MACD_hist"] > 0, 1, -1)
    return df

4.5 Combine Signals

def signal(df, carry_signal):
    df = regime(df)
    df["final_signal"] = (
        df["trend"] * 0.4 +
        df["momentum"] * 0.3 +
        carry_signal * 0.2 +
        df["vol_regime"] * 0.1
    )
    df["position"] = np.where(df["final_signal"] > 0.2, 1,
                        np.where(df["final_signal"] < -0.2, -1, 0))
    return df

Carry signal example:

carry_eurjpy = 1  # from yield differential API ideally
eurjpy = signal(eurjpy, carry_eurjpy)

4.6 Backtesting Logic

def backtest(df):
    df["return"] = df["Close"].pct_change()
    df["strategy"] = df["position"].shift(1) * df["return"]
    df["equity"] = (1 + df["strategy"]).cumprod()
    return df

5. Risk Management

5.1 ATR-Based Stop

stop = 2 * ATR
target = 3 * ATR

JPY pairs respond well to ATR-based stops because they normalize BOJ shocks.

5.2 Position Sizing

position_size = risk_per_trade / (ATR * pip_value)

Keep risk per trade below 1%.

6. Performance Expectations

A typical low-frequency JPY strategy should aim for:

  • Win rate: >45%–55%
  • Risk reward ratio: 1.5–2.5
  • Annualized return: 10–25%
  • Max DD: < 15%
  • Correlation: low vs equities

JPY pairs trend well after BOJ shocks;

They mean-revert well during liquidity traps;

And they respect macro regimes unusually cleanly.

7. Why This Strategy Works

(1) JPY’s unique role as global funding currency

It reacts cleanly to risk regimes.

(2) EUR/JPY and USD/JPY have strong trend persistence

Driven by yield differentials and risk sentiment.

(3) BOJ policy gives long-lasting market structure breaks

Perfect for swing models.

(4) Carry + trend + volatility + momentum is a powerful combination

Low false positives, high regime accuracy.

8. Conclusion

A low-frequency FX quant strategy on EUR/JPY and USD/JPY must integrate:

  • macro yield structure
  • BOJ/Fed/ECB divergence
  • volatility regime detection
  • technical trend confirmation
  • robust risk control

Using the Python framework above, you can scale this into a production quant engine — adding live data, OANDA/IBKR execution, and Bayesian regime estimation.

JPY is one of the most structurally predictable currencies in the world — if you understand the underlying macro drivers and marry them with disciplined quant execution.