Signals

Rolling Sharpe Ratio

Evaluate return quality over time on a rolling risk-adjusted basis.

What it is

Rolling Sharpe Ratio compares average excess return to return volatility over a moving window. It helps you judge whether recent performance has been efficient, not just positive.

When to use it

Comparing the quality of returns across symbols or strategy sleeves.
Checking whether a rally is still paying investors for the risk taken.
Adding a risk-adjusted overlay before rotating capital into stronger trends.

The maths

log_return[t] = ln(close[t] / close[t-1]), RSHARPE(N) = (mean(log_return, N) / std(log_return, N)) × sqrt(252) This is risk-adjusted return over a rolling window.

What it tells you

Values above 1.0 indicate good risk-adjusted returns. A falling rolling Sharpe while price rises suggests returns are getting noisier, which makes it useful for monitoring whether a strategy is deteriorating.

REST example

python

import os
import requests

response = requests.get(
  'https://api.financedata.com/v1/signals/RSHARPE/QQQ',
  params={
      'start_date': '2025-01-01',
      'end_date': '2025-04-30',
      'period': 60,
      'risk_free': 0.0,
      'periods_per_year': 252,
  },
  headers={'X-API-Key': os.environ['FDA_KEY']},
  timeout=30,
)
response.raise_for_status()
print(response.json())

MCP example

Tool call body

{
"name": "run_signals",
"arguments": {
  "symbols": ["QQQ"],
  "signal_names": ["RSHARPE"],
  "start_date": "2025-01-01",
  "end_date": "2025-04-30",
  "signal_parameters": {
    "RSHARPE": { "period": 60, "risk_free": 0.0, "periods_per_year": 252 }
  }
}
}

Rolling Sharpe is best requested through run_signals when you want the agent to compare risk-adjusted quality across several symbols in the same pass.

Agent prompt that triggers it

Compute the 60-day rolling Sharpe Ratio for QQQ and tell me whether recent returns still look attractive on a risk-adjusted basis.