Streams are processing asynchronous/concurrent (python asyncio) and each stream is started in a separate thread.
No use of the twisted module, so you can use this lib in a daemonized application (compatible with python-daemon).
If you can not store your data in cause of a temporary technical issue, you can kick back the data to the stream_buffer which stores the receives in the RAM till you are able to process the data in the normal way again.
Compatible with UnicornFy to convert received raw data from crypto API endpoints into well-formed python dictionaries.
Helpful management features like get_binance_api_status() , get_stream_info() , get_stream_list() , get_stream_statistic() , is_websocket_uri_length_valid() , replace_stream() , wait_till_stream_has_started() , get_current_receiving_speed() and many more, explore them here.
Following official documentation (https://github.com/binance-exchange/binance-official-api-docs/blob/masteweb-socket-streams.md), data is pushed down the stream EVERY SECOND. This seems kind of weird as the very reason to use sockets is to get real time updates when the data is pushed with EVERY EVENT. Wouldn't it be more efficient to poll REST API with higher then 1 request/s frequency (possibly from multiple threads using keep alive connection)? This way if we want to have the most up-to-date local order book we will avoid the 1s wait time on the socket. Curious to hear opinions of people who understand the context. Thanks!
For my bot I need to get 1m klines every minute on ~15 markets. I use websocket multiplex channel (e.g. [email protected]_1m/[email protected]_1m) to reduce REST API queries. But when I get new closed kline on all 15 markets ("x": true in response payload)? i get lag in total about 7-8 seconds every minute. Below is a common printout, repeating every minute:
2020-06-14 23:26:01 - INFO - New closed kline [2020-06-14 20:25:00] on [EOSBTC] 2020-06-14 23:26:01 - INFO - New closed kline [2020-06-14 20:25:00] on [BNBBTC] 2020-06-14 23:26:02 - INFO - New closed kline [2020-06-14 20:25:00] on [ETHBTC] 2020-06-14 23:26:04 - INFO - New closed kline [2020-06-14 20:25:00] on [ADABTC] 2020-06-14 23:26:05 - INFO - New closed kline [2020-06-14 20:25:00] on [LINKBTC] 2020-06-14 23:26:05 - INFO - New closed kline [2020-06-14 20:25:00] on [XMRBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [LTCBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [NEOBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [QTUMBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [IOTABTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [ETCBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [ZECBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [DASHBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [XRPBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [BATBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [XLMBTC] 2020-06-14 23:26:08 - INFO - New closed kline [2020-06-14 20:25:00] on [ATOMBTC]
First two klines come at 01 second of the next minute, next on 02, next on 04, then two on 05, rest on 08 sec. So I lose about 10 seconds in my decisions every minute. This lag is crucial for my strategy. I tried two python libs (https://github.com/oliver-zehentleitneunicorn-binance-websocket-api, https://python-binance.readthedocs.io/en/latest/websockets.html), ran the script on my laptop and remote server (google cloud), tried 2 and 12 CPU cores but all the time have the same result. When I work with 2-3 markets - it's OK and I have about 1 sec lag, but on 15 markets I have a very big lag about 7-8 secs. Is there any other way to get new closed klines just right after they were closed (in about 1 sec after it was closed)? Thank you!
I am wondering what is the correct way to to do api calls to binance for price and order book information (along with account trading). I see several flavours: rest api, websockets and a choice of libraries (eg ccxt). What are seasoned traders on binance using?
@cz_binance: RT @pushpendrakum: #Binance Api integration and websocket video second parts is coming in hindi. If you have not watched first part then you can watch here 👇👇 https://t.co/mU2vU3vfGO Cc: @cz_binance @binance
@binance: #BinanceFutures Api Update - New WebSocket streams for book tickers added - New WebSocket streams for partial order book added - Faster diff data with 100ms updates: - Added Update Speed: to Websocket Market Streams https://t.co/qiAQzZrHWT
Binance upgrade will reportedly target three key areas of the platform. It will enhance the API, Matching Engine, and Websocket performance optimizations. Thus, the platform will have enhanced operability standards once the upgrade completes satisfacto
@binance: ICYMI #Binance will perform a scheduled system upgrade at 2019/08/15 02:00 AM (UTC) taking approximately 6-8 hours. Improvements include: Matching Engine, API, & WebSocket performance OCO (Order Cancels Order), available via API 5,000 & 10,000 order book levels from Rest API
Will Binance API ever support an Websocket order API?
The REST order API is often too slow for my needs, even with a VM in AWS Tokyo. Also I would like to know if they will ever support a real-time ticker feed over websockets. Currently it sends info out 1 time a second. Given their rate limit of 20 requests a second, it is much faster to just poll the REST API 10 or so time a second than use the websocket ticker.
Would anyone use an API that returns an up-to-the-minute Pandas DataFrame of kline data?
What is this?
This is an API for serving updated kline data as pandas dataframes directly from Binance. I know a lot of systems use a database, but I found that expensive and slow with large amounts of data. Right now, I have a beta version running that handles Binance, but other exchanges will be added as needed.
What it does
It listens to the kline datastream via websockets and updates the dataframe every time a kline comes in as "closed", making sure it always has the latest data.
Why would you want this?
Managing all this all this data efficiently, is tricky and expensive. This API would allow you run your strategy live with up-to-date pricing data, which is exactly what I use it for.
What I'm offering
Access to this system as a REST API, but open to suggestions (web sockets, shared Redis database, etc).
It's free. It's certainly beta right now, so you won't want to use it in production, yet.
Hi, Will Binance release an API for leveraged tokens? At the moment, correct me if I'm wrong, but I believe there are no endpoints or websockets to see details such as NAV, basket, leverage, rebalancing history, etc.
Building order book and matching engine from websocket feed
I'm trying to subscribe to a real-time feed of trades so I can build an order book and matching engine for learning purposes. I can connect to the hitbtc websocket API and subscribe to trades, but I can't see anything in the docs for how a limit order would be cancelled? According to the docs, a trade update looks like:
So I assume that's a specific limit order with specific ID. But how would I know if it's cancelled, modified or fulfilled? Looking at other websocket APIs (coinbase, binance) I can't see how it would be supported in those either. HitBTC API - https://api.hitbtc.com/#subscribe-to-trades
I applied price discovery algorithms to 5 Min OHLCV data from Bitmex and CME contracts and Bitstamp, Coinbase, HitBTC, Kraken, Poloniex, Binance, and OkEx BTCUSD/BTCUSDT markets from March 2016 to May 2020. Some exciting results I got was:
Before the 2017/18 bull run, Bitfinex dominated the price discovery process. They started the run. But as the price increased, trades on other exchanges, Binance and Bitstamp played a more dominant role in leading the price up.
Since then, CME Contracts and Bitmex contracts have had an increasing role in price discovery. Today Bitmex and CME Contracts play the most substantial role in determining the direction of Bitcoin price.
In 2020, market dominance by Bitmex has been negatively correlated with price. Dominance by Bitfinex, Huobi and OkCoin has had high positive correlation with price.
Price discovery is the overall process of setting the price of an asset. Price discovery algorithms identify the leader exchanges whose traders define the price. Two approaches are most famous for use in Price Discovery. Gonzalo and Granger (1995) and Hasbrouck (1995). But they assume random walk, and a common efficient price. I do not feel comfortable assuming random walk and common efficient price in Bitcoin Markets. So I used this little know method by De Blasis (2019) for this analysis. This work assumes that "the fastest price to reflect new information releases a price signal to the other slower price series." I thought this was valid in our market. It uses Markov Chains to measure Price Discovery. Without going into the mathematical details the summary steps used was:
Data is first grouped into a daily interval. Then inside each daily interval's 5-minute candles, the change in prices between the current time t and previous time t-1 is calculated. The difference across the same time t across all exchanges in a given day is juxtaposed to create an initial matrix.
The initial matrix is used to create a Transition Matrix, which measures the probability of price changing to something else at time t+1 for its state at t.
Then other Markov Chain based algorithms are used to measure the influence an exchange at time t had over all other exchanges' price movement at time t+1 individually.
Reduction and normalization is done to this data. In the end, each exchange receives a single number that sums to 1 for a given day.
De Blasis (2019) names this number Price Leadership Share (PLS). High PLS indicates a large role in price discovery. As the sum of the numbers is 1, they can be looked at as a percentage contribution. I recommend reading the original paper if you are interested to know more about the mathematical detail.
Andersen (2000) argues that 5 Minute window provides the best trade-off between getting enough data and avoiding noise. In one of the first work on Bitcoin's Price Discovery, Brandvold et al. 2015 had used 5M window. So I obtained 5M OHLCV data using the following sources:
Poloniex, Bitfinex, Binance and HitBTC: Exchange's API through CCXT.
CME: Okay, this was was supposed to be tricky and expensive. I broke a TOS and scraped the data for free, removing the expensive part from the equation. I will not go into detail about where I scraped this data.
Futures data are different from other data because multiple futures contract trades at the same time. I formed a single data from the multiple time series by selecting the nearest contract until it was three days from expiration. I used the next contract when the contract was three days from expiration. This approach was advocated by Booth et al ( 1999 )
I can't embed the chart on reddit so open this https://warproxxx.github.io/static/price_discovery.html In the figure above, each colored line shows the total influence the exchange had towards the discovery of Bitcoin Price on that day. Its axis is on the left. The black line shows a moving average of the bitcoin price at the close in Bitfinex for comparison. The chart was created by plotting the EMA of price and dominance with a smoothing factor of 0.1. This was done to eliminate the noise. Let's start looking from the beginning. We start with a slight Bitfinex dominance at the start. When the price starts going up, Bitfinex's influence does too. This was the time large Tether printing was attributed to the rise of price by many individuals. But Bitfinex's influence wanes down as the price starts rising (remember that the chart is an exponential moving average. Its a lagging indicator). Afterward, exchanges like Binance and Bitstamp increase their role, and there isn't any single leader in the run. So although Bitfinex may have been responsible for the initial pump trades on other exchanges were responsible for the later rally. CME contracts were added to our analysis in February 2018. Initially, they don't have much influence. On a similar work Alexandar and Heck (2019) noted that initially CBOE contracts had more influence. CBOE later delisted Bitcoin futures so I couldn't get that data. Overall, Bitmex and CME contracts have been averaging around 50% of the role in price discovery. To make the dominance clear, look at this chart where I add Bitmex Futures and Perp contract's dominance figure to create a single dominance index. There bitmex leads 936 of the total 1334 days (Bitfinex leads 298 days and coinbase and binance get 64 and 6 days). That is a lot. One possible reason for this might be Bitmex's low trading fee. Bitmex has a very generous -0.025% maker fee and price discovery tend to occur primarily in the market with smaller trading costs (Booth et al, 1999). It may also be because our market is mature. In mature markets, futures lead the price discovery.
Table 1: Days Lead
Out of 1334 days in the analysis, Bitmex futures leads the discovery in 571 days or nearly 43% of the duration. Bitfinex leads for 501 days. Bitfinex's high number is due to its extreme dominance in the early days.
Table 2: Correlation between the close price and Exchange's dominance index
Binance, Huobi, CME, and OkCoin had the most significant correlation with the close price. Bitmex, Coinbase, Bitfinex, and Bitstamp's dominance were negatively correlated. This was very interesting. To know more, I captured a yearwise correlation.
Table 3: Yearwise Correlation between the close price and Exchange's dominance index Price movement is pretty complicated. If one factor, like a dominant exchange, could explain it, everyone would be making money trading. With this disclaimer out of the way, let us try to make some conclusions. This year Bitfinex, Huobi, and OkEx, Tether based exchanges, discovery power have shown a high correlation with the close price. This means that when the traders there become successful, price rises. When the traders there are failing, Bitmex traders dominate and then the price is falling. I found this interesting as I have been seeing the OkEx whale who has been preceding price rises in this sub. I leave the interpretation of other past years to the reader.
My analysis does not include market data for other derivative exchanges like Huobi, OkEx, Binance, and Deribit. So, all future market's influence may be going to Bitmex. I did not add their data because they started having an impact recently. A more fair assessment may be to conclude this as the new power of derivative markets instead of attributing it as the power of Bitmex. But Bitmex has dominated futures volume most of the time (until recently). And they brought the concept of perpetual swaps.
There is a lot in this data. If you are making a trading algo think there is some edge here. Someday I will backtest some trading logic based on this data. Then I will have more info and might write more. But, this analysis was enough for to shift my focus from a Bitfinex based trading algorithm to a Bitmex based one. It has been giving me good results. If you have any good ideas that you want me to write about or discuss further please comment. If there is enough interest in this measurement, I can setup a live interface that provides the live value.
binance.py a new python library powered by modern technologies
Hey there, I'm here to present a project in which I've invested a lot of time, but first I'll tell you how I got here. A few weeks ago I was contacted by a client who needed me to develop a trading bot capable of managing several accounts at the same time. I started using python-binance but I was not very satisfied because it didn't support asyncio (well there was a branch in development but it seemed to be abandoned). So I started to develop my alternative by trying to create the library I would have liked to have. As I thought it could help some people I decided to make this library completely free (and opensouce of course). Here is the github link: https://git.io/binance.py Currently binance.py completely covers:
the Rest API
userDataStreams, websockets (events)
I'd love to get your feedback. If you need help with it, I'd be very happy to help you. I could also introduce support for the margin trading API and the withdraw API if anyone needs it. Sincerely, Thomas!
Binance Chain Technology Documentation. 6. Book Depth Streams. Top 20 (could extend to 100, 500, 1000) levels of bids and asks. Change Log. 2020-08-18. WEBSOCKET. New optional baseurl for websocket market streams and user data streams: wss://fstream3.binance.com The usage is exactly the same as the existing baseurl wss://fstream.binance.com. A python API to use the Binance Websocket API's (com, com-margin, com-futures, jersey, us, jex, dex/chain+testnet) in a easy, fast, flexible, robust and fully-featured way. Messages are received as dictionary objects relating to the message formats defined in the Binance WebSocket API documentation. Websockets are setup to reconnect with a maximum of 5 retries. The limits on the API are based on the IPs, not the API keys. It is strongly recommended to use websocket stream for getting data as much as possible, which can not only ensure the timeliness of the message, but also reduce the access restriction pressure caused by the request. Order Rate Limits
How to stream Binance Websockets data into browser