Signals

WMA — Weighted Moving Average

Use a weighted average to react faster to fresh price action.

What it is

Weighted Moving Average emphasizes the newest closes more than the oldest ones, so it reacts faster than SMA without becoming as jumpy as very short momentum signals.

When to use it

Tracking short-term trend shifts while still smoothing noisy daily closes.
Testing faster moving-average crossovers against a slower baseline.
Building pullback rules that need a more responsive trend anchor.

The maths

WMA(N) = (N×close[t] + (N-1)×close[t-1] + ... + 1×close[t-N+1]) / (N + (N-1) + ... + 1)

What it tells you

WMA reacts faster than SMA by weighting recent prices more heavily. It offers less lag than SMA while staying more stable than EMA, so it is a useful smoother in crossover systems.

REST example

python

import os
import requests

response = requests.get(
  'https://api.financedata.com/v1/signals/WMA/AAPL',
  params={'start_date': '2025-01-01', 'end_date': '2025-04-30', 'period': 20},
  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": ["AAPL"],
  "signal_names": ["WMA"],
  "start_date": "2025-01-01",
  "end_date": "2025-04-30",
  "signal_parameters": {
    "WMA": { "period": 20 }
  }
}
}

WMA is available in MCP through the batch run_signals tool; pass its lookback under signal_parameters.WMA.

Agent prompt that triggers it

Run a 20-day WMA for AAPL and tell me whether price is holding above or below the weighted trend line right now.