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This article mainly explains "how to download and analyze futures position data with Python". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to download and analyze futures position data with Python.

Futures position report

Futures position report, referred to as COT (Commitment of Traders) report, records futures position data of institutional investors, including commercial companies and hedge funds. Announced by the United States Futures Trading Commission (CFTC), the announcement time is 02:30 every Friday afternoon (US Eastern time).

We focus on the traditional format (Legacy Format) of the COT report, which summarizes position data for futures and options.

COT reports in traditional format contain the following data:

Commercial position (Commercial): futures positions of product manufacturers / sellers, divided into long positions and short positions, are used to hedge against price fluctuations.

Non-commercial positions (Noncommercial): futures positions of hedge funds, investment banks and large individual players, divided into long and short positions, referred to as "speculative positions".

Long position (Long): number of long contracts.

Short: the number of short contracts.

Open contract (Open Interest): the number of outstanding contracts outstanding.

No need to report reserve position (Non-reportable Position): the number of open contracts that do not meet the requirements of CFTC, which refers to the position held by small players.

1. Prepare data

Download the COT report from Quandl.

Quandl is a financial data provider, there are a large number of free datasets available, users need to apply for an API key first.

In order to get data easily with Python, install the tripartite library 'quandl'. first

two。 Speculative position

View the non-commercial long, short and net positions of a single futures product.

Net position in non-commercial futures = long in non-commercial futures-short in non-commercial futures.

3. Short-term position change

Calculate the percentage growth of speculative bulls or bears of all futures varieties, compare the growth rate of the latest period horizontally, and observe the changes in short-term market sentiment.

4. COT index

A commonly used derivative indicator is the COT index, which measures market sentiment based on long and short positions in non-commercial futures.

Calculation formula: $$ci_t =\ frac {netpos_t-min (netpos)} {max (netpos)-min (netpos)} $$

$ci_t$ is the COT index of period t

$netpos_t$ is the net position of non-commercial futures in period t

$min (netpos) $is the minimum net position in non-commercial futures for the past K period.

$max (netpos) $is the maximum net position in non-commercial futures for the past K period.

The range of COT index is $[0,1] $, the closer to 0, the stronger the bearish sentiment, the closer to 1, the stronger the bullish sentiment.

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