Amazon Price History API: Buy Box, FBA and FBM Price History as JSON

To get an Amazon product's price history as JSON, call the SellerMagnet /api/amazon-product-statistics endpoint with the ASIN and marketplace. One request returns the recorded Buy Box, Amazon-as-seller, lowest FBA and lowest FBM price series under data.stats, each as [date, price] pairs in integer minor units, plus sales rank, rating and monthly-sold history. Add graphs=true and the same credit also returns a PNG chart per series.

Which price histories does the API return?

The statistics endpoint returns recorded history, not a live snapshot - for the live Buy Box and the full offer list, use the product offers endpoint. The price series differ in one way that matters for comparisons: whether shipping is included.

Next to the prices, stats also holds salesRankHistory (timestamps with a time of day; -1 means the product had no rank), monthlySoldHistory as ["YYYY-MM", units], and rating and review-count histories. data.trackingSince gives the date recording started, or N/A - that is how far back the history goes.

History covers amazon.com, .ca, .com.mx, .com.br, .co.uk (also used for Ireland), .de, .fr, .it, .es, .in and .co.jp. For any other marketplace the response describes amazon.com, so check data.marketplaceId before you store it.

Response from /api/amazon-product-statistics (trimmed, example values)

{
  "success": true,
  "data": {
    "asin": "B0CLTBHXWQ",
    "marketplaceId": "APJ6JRA9NG5V4",
    "buyBoxPrice": 41800,
    "buyBoxFulfillment": "FBM",
    "lowestFBAPrice": 44999,
    "trackingSince": "2023-12-18",
    "stats": {
      "buyBoxPriceHistory": [["2025-06-12", 42900], ["2025-06-13", 41700], ["2025-06-13", 41800]],
      "lowestFBAPriceHistory": [["2025-06-14", 44999]],
      "amazonAsSellerPriceHistory": [["2025-06-14", 44999]],
      "salesRankHistory": [["2025-06-14 01:58:00", 15]],
      "monthlySoldHistory": [["2025-05", 1000], ["2025-06", 1000]]
    }
  }
}

How do I turn the price history into a daily table?

A series records a value each time the price changed, so a busy day has several entries and a quiet week has none. For charts and comparisons, keep the last price per day and carry it forward until the next change. One caveat: the moments when nobody offered the product are removed from the series, so a quiet stretch can mean the price held or that the product was unavailable. Forward-filling assumes it held; if availability matters, check the seller's stockHistory in data.offers.

price_history.py - fetch, convert and compute the lows

import datetime as dt
import os

import requests

ZERO_DECIMAL = {"A1VC38T7YXB528"}  # amazon.co.jp: values are whole yen


def statistics(asin: str, marketplace_id: str) -> dict:
    resp = requests.get(
        "https://sellermagnet-api.com/api/amazon-product-statistics",
        params={"asin": asin, "marketplaceId": marketplace_id,
                "api_key": os.environ["SELLERMAGNET_API_KEY"]},
        timeout=90,
    )
    try:
        body = resp.json()
    except ValueError:  # e.g. an HTML error page from a proxy
        body = {}
    if resp.status_code != 200 or not body.get("success"):
        raise RuntimeError(f"{asin}: {resp.status_code} {body.get('message')}")
    return body["data"]


def daily(series: list, marketplace_id: str) -> dict:
    """[[date, minor units], ...] -> {date: price}, keeping the last value per day."""
    divisor = 1 if marketplace_id in ZERO_DECIMAL else 100
    out = {}
    for day, value in series:  # already in time order
        out[day[:10]] = value / divisor
    return out


def low_since(prices: dict, days: int) -> float | None:
    """Lowest price in effect during the last `days` days (steps carried forward)."""
    cutoff = (dt.date.today() - dt.timedelta(days=days)).isoformat()
    before = [p for d, p in prices.items() if d < cutoff]
    recent = [p for d, p in prices.items() if d >= cutoff]
    if before:
        recent.append(before[-1])  # the price still in effect when the window opened
    return min(recent) if recent else None


data = statistics("B0CLTBHXWQ", "APJ6JRA9NG5V4")
buy_box = daily(data["stats"]["buyBoxPriceHistory"], data["marketplaceId"])
print("tracking since", data.get("trackingSince"))
print("30-day low", low_since(buy_box, 30), "90-day low", low_since(buy_box, 90))

Is the Buy Box price history comparable with the FBA price?

Not directly. buyBoxPriceHistory is the landed price - item price plus shipping - while lowestFBAPriceHistory is the item price alone. For an FBA offer that ships free the two agree; for a merchant-fulfilled offer with shipping they do not. Compare landed with landed (buyBoxPriceHistory and lowestFBMPriceHistory), or read each seller's item price and shipping separately from data.offers, where every priceHistory entry is [timestamp, price, shipping].

Can the API return price history charts?

Yes. Pass graphs=true and the response gains data.graphs: one PNG URL per series that has data, such as buyBoxPriceHistory, lowestFBAPriceHistory, salesRankHistory and monthlySoldHistory. The request still costs one credit. The images carry a watermark, so they suit internal dashboards and reports better than a storefront.

Chart URLs for one ASIN with curl and jq

curl -sG "https://sellermagnet-api.com/api/amazon-product-statistics" \
  --data-urlencode "asin=B0CLTBHXWQ" \
  --data-urlencode "marketplaceId=APJ6JRA9NG5V4" \
  --data-urlencode "graphs=true" \
  --data-urlencode "api_key=$SELLERMAGNET_API_KEY" \
  | jq -r '.data.graphs | to_entries[] | "\(.key)\t\(.value)"'

For a watch list, run the script once a day, compare today's price with the 90-day low and alert when it drops below it. Recorded history changes slowly, so there is no point checking it more often; for reacting to a live Buy Box change, the Buy Box polling guide covers intervals and cost. To pull history for thousands of ASINs, see the bulk lookup guide.

Bottom line: one request, every recorded series; divide by 100 (not for yen), compare landed with landed, and keep the last price per day. The product statistics page shows the endpoint, and a free account includes 500 credits.