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Time Series

Forecast data is served in a similar way to Signal data but the query approach is much more explicit. The endpoint to access the data is Get Forecast Data:

http://api.clearmacro.com/api/v3/forecast/market/{market_code}/currency/{currency_iso}/horizon/{horizon_name}/data/

Which takes the path parameters:

market_code: The market code you want a forecast for
currency_iso: The currency iso code of the forecast
horizon_name: The time horizon you want the forecast over

Values for all of these parameters can be found by using the other informational forecast endpoints.

By default the data will be returned in Parquet format which can be easily read and manipulated using the Pandas framework in Python.

import io

import pandas as pd
import requests

url = "https://api.clearmacro.com/api/v3/forecast/market/EQTUS/currency/USD/horizon/10%20Years/data/?fmt=parquet"
headers = {"X-API-KEY": "YOUR_API_KEY"}
response = requests.get(url=url, headers=headers)

if 200 <= response.status_code < 299:
    # On successful request load data into to pandas dataframe
    dataframe = pd.read_parquet(io.BytesIO(response.content))
else:
    # If the request failed, print the error code and message
    print("Error occurred")
    print("{response.status_code}: {response.content}")

Data Structure

The data structure of a forecast dataframe is much more complex than signals and supports numerous columns and will look something like this:

| Date       | Forecast |  _Trend   | _Trend_Growth | _Trend_Income | _Trend_Valuation |
|------------|----------|-----------|---------------|---------------|------------------|
| 1990-07-02 | 0.083477 |  0.083477 | 0.056477      | 0.030647      | -0.003648        |
| 1990-07-03 | 0.083493 |  0.083493 | 0.056456      | 0.030655      | -0.003619        |
| 1990-07-04 | 0.083508 |  0.083508 | 0.056436      | 0.030663      | -0.003591        |
| 1990-07-05 | 0.084066 |  0.084066 | 0.056416      | 0.031064      | -0.003414        |
| 1990-07-06 | 0.083538 |  0.083538 | 0.056396      | 0.030679      | -0.003537        |
| ...          ...         ...       ...              ...             ...
| 2023-08-20 | 0.056501 |  0.056501 | 0.053200      | 0.017416      | -0.014116        |
| 2023-08-21 | 0.056237 |  0.056237 | 0.053200      | 0.017342      | -0.014305        |
| 2023-08-22 | 0.056251 |  0.056251 | 0.053200      | 0.017416      | -0.014365        |
| 2023-08-23 | 0.055592 |  0.055592 | 0.053200      | 0.017194      | -0.014803        |
| 2023-08-24 | 0.055592 |  0.055592 | 0.053200      | 0.017194      | -0.014803        |

The columns you will find in our Forecast dataframes are as follows:

  1. Forecast - Our overall forecast for a market.
  2. _Trend - The Trend contribution to our overall forecast.
  3. _Trend_Growth - The Growth contribution to our Trend forecast.
  4. _Trend_Income - The Income contribution to our Trend forecast.
  5. _Trend_Valuation- The Valuation contribution to our Trend forecast.
  6. Cyclical - The Cyclical contribution to our overall forecast
  7. _Cyclical_Contraction - The Contraction contribution to our Cyclical forecast.
  8. _Cyclical_Recovery - The Recovery contribution to our Cyclical forecast.
  9. _Cyclical_Expansion - The Expansion contribution to our Cyclical forecast.
  10. _Cyclical_Slowdown - The Slowdown contribution to our Cyclical forecast.
  11. Tactical - The Tactical contribution to our overall forecast.
  12. _Tactical_Fundamentals - The Fundamentals contribution to our Tactical forecast.
  13. _Tactical_Market_Technicals - The Market Technicals contribution to our Tactical forecast.

You will find that each contribution level sums to that levels stated forecast.

For example:

Forecast is the sum of Trend, Cyclical & Tactical. Trend is the sum of _Trend_Growth, _Trend_Income & _Trend_Valuation

Approach

You can find a detailed explaination about our overall methodology to generating forecasts and what each of the columns represent here.

Alternative data formats

As with Signals, if you wish to load the data into Excel or work on it in another programming language that does not have a dataframe analogue; you can also request the data in CSV or JSON formats by using the fmt parameter.

curl -X "GET" \
https://api.clearmacro.com/api/v3/forecast/market/EQTUS/currency/USD/horizon/10%20Years/data/?fmt=json
-H "X-API-KEY:$API_KEY" > EQTUS_USD_10_YEARS.json
note

More information about each data format can be found using the following links: