
China Retail Investor Sentiment - Funds | Alternative Data | Daily Update | ETFs | 23000+ Funds
# | fund_id |
fund_type_id |
pub_date |
read_neg_sum |
reply_neg_sum |
post_neg_sum |
user_neg |
user_avg_bar_age_neg |
read_neu_sum |
reply_neu_sum |
post_neu_sum |
user_neu |
user_avg_bar_age_neu |
read_pos_sum |
reply_pos_sum |
post_pos_sum |
user_pos |
user_avg_bar_age_pos |
read_all_sum |
reply_all_sum |
post_all_sum |
user_all |
user_avg_bar_age_all |
senti_score_div |
senti_score_log |
senti_conform |
delivery_time |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx |
2 | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx |
4 | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx |
5 | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx |
6 | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
7 | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx |
8 | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx |
9 | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx |
10 | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
... | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx |
# | post_id |
user_id |
fund_id |
fund_type_id |
post_time |
post_location |
sentiment |
char_count |
chinese_char_count |
emoji_count |
delivery_time |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx |
2 | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx |
3 | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx |
4 | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx |
5 | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx |
6 | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx |
7 | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx |
8 | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx |
9 | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx |
10 | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx |
... | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
fund_id
|
Integer | 1 | |
fund_type_id
|
Integer | 52 | |
pub_date
|
DateTime | 2024-03-01T00:00:00+00:00 | |
read_neg_sum
|
Integer | 0 | |
reply_neg_sum
|
Integer | 0 | |
post_neg_sum
|
Integer | 0 | |
user_neg
|
Integer | 0 | |
user_avg_bar_age_neg
|
Integer | 0 | |
read_neu_sum
|
Integer | 0 | |
reply_neu_sum
|
Integer | 0 | |
post_neu_sum
|
Integer | 0 | |
user_neu
|
Integer | 0 | |
user_avg_bar_age_neu
|
Integer | 0 | |
read_pos_sum
|
Integer | 947 | |
reply_pos_sum
|
Integer | 1 | |
post_pos_sum
|
Integer | 3 | |
user_pos
|
Integer | 3 | |
user_avg_bar_age_pos
|
Integer | 1935 | |
read_all_sum
|
Integer | 947 | |
reply_all_sum
|
Integer | 1 | |
post_all_sum
|
Integer | 3 | |
user_all
|
Integer | 3 | |
user_avg_bar_age_all
|
Integer | 1935 | |
senti_score_div
|
Float | 1.0 | |
senti_score_log
|
Float | 1.38629 | |
senti_conform
|
Float | 1.0 | |
delivery_time
|
DateTime | 2024-03-02T02:00:00+00:00 |
Attribute | Type | Example | Mapping |
---|---|---|---|
post_id
|
String | 518f36c2df14a8c6 | |
user_id
|
String | 414b3f085bf6a847 | |
fund_id
|
Float | 2418.0 | |
fund_type_id
|
Integer | 52 | |
post_time
|
DateTime | 2024-03-01T00:00:05+00:00 | |
post_location
|
Float | ||
sentiment
|
Integer | -1 | |
char_count
|
Integer | 43 | |
chinese_char_count
|
Integer | 21 | |
emoji_count
|
Integer | 0 | |
delivery_time
|
DateTime | 2024-03-02T02:00:00+00:00 |
Description
Country Coverage
History
Volume
23,000 | Funds |
1,000 | ETF |
Pricing
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds?
Gain insights into the interest and sentiment of China-based funds and ETFs by analyzing data from leading local financial social media platforms. This dataset offers sentiment analytics and hotness metrics across 23,000+ funds and 1,000+ ETFs to identify trends and market sentiment effectively.
What is China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds used for?
This product has 4 key use cases. Datago Technology Limited recommends using the data for Systematic Trading, Quantitative Investing, Alternative Investment, and Financial Data Enrichment. Global businesses and organizations buy Alternative Data from Datago Technology Limited to fuel their analytics and enrichment.
Who can use China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Alternative Data. Get in touch with Datago Technology Limited to see what their data can do for your business and find out which integrations they provide.
How far back does the data in China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds go?
This product has 15 years of historical coverage. It can be delivered on a daily basis.
Which countries does China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds cover?
This product includes data covering 1 country like China. Datago Technology Limited is headquartered in Hong Kong.
How much does China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds cost?
Pricing information for China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds is available by getting in contact with Datago Technology Limited. Connect with Datago Technology Limited to get a quote and arrange custom pricing models based on your data requirements.
How can I get China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds?
Businesses can buy Alternative Data from Datago Technology Limited and get the data via S3 Bucket and SFTP. Depending on your data requirements and subscription budget, Datago Technology Limited can deliver this product in .json and .csv format.
What is the data quality of China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds?
You can compare and assess the data quality of Datago Technology Limited using Datarade’s data marketplace.
What are similar products to China Retail Investor Sentiment - Funds Alternative Data Daily Update ETFs 23000+ Funds?
This product has 3 related products. These alternatives include Alternative Data Social Media-Based Insights on 800M+ Professionals & Companies for VC, Hedge Funds & Investment Analysis, PredSearch Alternative Data, Hedge Funds Data, Stock Market Data, Investors Data Consumer Stocks Signal Global coverage 100+ Tickers, and China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update. You can compare the best Alternative Data providers and products via Datarade’s data marketplace and get the right data for your use case.