What is Sharpe Ratio?

In Plain English

Suppose two portfolio managers both returned 15% last year. Before you hand either of them more capital, you need to ask: how much risk did they take to get there? A manager who returned 15% with wild daily swings is doing something very different — and far less reproducible — than one who delivered the same return with smooth, consistent gains. The Sharpe ratio is the tool that answers this question.

Think of it like fuel efficiency in cars. Miles per gallon tells you how far you can go per unit of fuel burned. The Sharpe ratio tells you how much return you earned per unit of risk taken. A high Sharpe means you're getting a lot of return for the volatility you're accepting. A low Sharpe means the ride is bumpy relative to the destination.

The calculation subtracts the risk-free rate (typically the 3-month Treasury yield) from the portfolio's return to get the "excess return" — what you earned above what you could have gotten from a riskless investment. Then it divides by the standard deviation of returns. The resulting ratio lets you compare strategies across different asset classes, time periods, and position sizes on a common scale.

Practitioners generally consider a Sharpe above 1.0 good, above 2.0 excellent, and above 3.0 exceptional. Sustained Sharpe ratios above 3.0 over multi-year periods are exceedingly rare — a few celebrated quant funds have achieved them, typically by combining many uncorrelated signals.

Improving the Sharpe ratio of a systematic strategy is one of the primary use cases for alternative data. Adding a signal with positive alpha that is uncorrelated with your existing signals raises the information ratio (the Sharpe analog for active managers) even if the new signal has only modest standalone performance.

Technical Definition

The Sharpe ratio S for a portfolio with returns \{r₁, r₂, ..., r_T\} over period T:

S = (r̄_p − r_f) / σ_p

For annualization: multiply the numerator by the annualization factor and the denominator by √factor. For daily returns: annualized Sharpe = (daily Sharpe) × √252.

The Sharpe ratio assumes returns are normally distributed. Strategies with fat-tailed return distributions (e.g. short volatility) can display artificially high Sharpe ratios that do not reflect their true risk. The Sortino ratio addresses this by using only downside deviation in the denominator. The Calmar ratio uses maximum drawdown instead.

Ex-ante Sharpe (using expected return and forecast volatility) differs from ex-post Sharpe (realized). GARCH-based volatility forecasts improve ex-ante estimates over simple historical volatility.

How VectorFin Uses This

VectorFin's signals/volatility table provides GARCH-based volatility forecasts at 1-day, 5-day, and 21-day horizons for each ticker. These forecasts feed directly into ex-ante Sharpe ratio calculations:

estimated_sharpe = expected_return / garch_vol_21d

Systematic strategies using VectorFin's whystock_score signal can use the GARCH forecasts to size positions inversely proportional to predicted volatility — the classic "vol targeting" approach — which tends to stabilize realized Sharpe ratios by reducing position sizes ahead of volatile periods.

Query the volatility signal at:

GET https://api.vectorfinancials.com/v1/signals/volatility/{ticker}?date=2024-10-01

Code Example

import requests
import numpy as np
import pandas as pd

API_BASE = "https://api.vectorfinancials.com"
API_KEY = "vf_your_api_key_here"

def get_volatility_signal(ticker: str, start: str, end: str) -> pd.DataFrame:
    resp = requests.get(
        f"{API_BASE}/v1/signals/volatility/{ticker}",
        params={"start": start, "end": end},
        headers={"X-API-Key": API_KEY},
    )
    resp.raise_for_status()
    return pd.DataFrame(resp.json()["data"])

# Fetch GARCH vol forecasts for position sizing
vol_df = get_volatility_signal("SPY", "2024-01-01", "2024-12-31")

# Vol-targeted position size: target 10% annualized vol
target_vol = 0.10
vol_df["position_size"] = target_vol / vol_df["garch_vol_21d"]
vol_df["position_size"] = vol_df["position_size"].clip(0.5, 2.0)  # limit leverage

# Hypothetical return stream
np.random.seed(42)
daily_returns = np.random.normal(0.0005, 0.015, len(vol_df))
vol_targeted_returns = daily_returns * vol_df["position_size"].values

# Compute annualized Sharpe
sharpe = (vol_targeted_returns.mean() * 252) / (vol_targeted_returns.std() * np.sqrt(252))
print(f"Annualized Sharpe (vol-targeted): {sharpe:.2f}")