In the world of financial trading, having access to accurate and timely information is crucial. The key to seizing profitable opportunities lies in real-time data. The faster the data updates, the more potential opportunities you can uncover. That’s why tick data holds such importance in high-frequency trading. Compared to conventional market data, tick data provides a much more granular view of market movements, offering traders a deeper, more comprehensive perspective.
Let’s start with a basic question: What is tick data?
What is Tick Data?
The price charts we usually see—like candlestick charts—are based on fixed time intervals. Tick data, on the other hand, captures each and every price change, regardless of when it occurs. For example, if a stock changes price 30 times within a single minute, a one-minute candlestick chart would only display four data points:
- Opening price
- Closing price
- Highest price
- Lowest price
These four values are all that’s needed to draw a candlestick.
The other 26 price changes that occurred in that minute are ignored.
Tick data does things differently—it records every price change. So in that same one-minute span, all 30 price movements would be captured, making tick data a form of high-frequency data.
High-frequency data allows us to observe market behavior and microstructure in greater detail. It enables the exploration of trading strategies over extremely short time intervals. In contrast, low-frequency data, which samples at longer intervals, often lacks the detail required for in-depth market analysis. Daily data, for instance, may miss out on important short-term fluctuations entirely.
What might seem like noisy or chaotic price movements actually contains a wealth of valuable information. Traditional market data might update every minute, but tick data updates in real time—recording every trade as it happens. These updates don’t occur at fixed intervals but are random, depending on when trades occur. This irregular timing allows us to infer market conditions like volatility and liquidity based on the time gaps between trades.
If you have a long-term tick dataset—say, five years’ worth—you can use it for robust backtesting.
Use Cases for Tick Data
Tick data plays a key role in many aspects of quantitative trading. One of the most common applications is backtesting.
Backtesting is essential in quantitative finance. It’s the process of applying a trading strategy to historical data to evaluate its performance. If a strategy performs well on past data, there’s a reasonable expectation it might do well in current or future markets. Tick data creates a realistic simulation of historical markets for this very purpose.
Tick data also supports risk management. Its granularity helps traders evaluate factors like liquidity, slippage, and execution quality. For researchers studying market microstructure, tick-level insights are indispensable. Even in legal and compliance contexts, financial institutions often need access to tick data to meet regulatory and reporting standards.
Understanding Tick Data Quality
As with most data products, not all tick data is created equal. Quality can vary, and poor-quality tick data can undermine everything from backtests to live strategies. Here are some common signs of corrupted or low-quality tick data:
- Data gaps: Missing ticks due to network or system failures.
- Invalid trades: Prices of zero or even negative values, e.g., a zero price for USDEUR—clearly a data error.
- Duplicate ticks: Identical price entries at the same timestamp, often caused by recording or system issues.
- Timestamp spoofing: Artificially inflated tick counts using repeated timestamps without actual price changes. Some providers manipulate data this way to give the appearance of faster updates.
Aside from human manipulation, the main reason for tick data corruption is sheer volume. In forex, for example, a single currency pair might see thousands of trades per day. Aggregated across multiple instruments, this adds up to millions of records in just a week or two. Signal quality issues, packet loss, or transmission delays can further degrade the dataset.
Cleaning and verifying such large datasets is no small feat. Excel is usually not equipped to handle this volume. Most providers turn to specialized tools like Google’s OpenRefine or use Python libraries like Pandas to script custom data cleaning workflows.
Market Differences in Tick Data
Not all markets generate the same kind of tick data. As mentioned earlier, forex markets are decentralized and harder to track. In contrast, centralized markets—like equities and commodities—route all trades through exchanges, which reliably record every tick. These structural differences influence both the quantity and quality of available data.
How Does AllTick Ensure Tick Data Quality?
As a professional provider, AllTick understands that accuracy, reliability, and consistency are non-negotiable when it comes to tick data. We take a customer-centric approach, knowing how critical this data is for sound analysis and informed trading decisions.
Some providers re-send the same timestamps repeatedly, while others cherry-pick one price out of many and stamp it with a new time. Both practices degrade data quality and can compromise long-term strategy development.
We do things differently:
- We collect tick data from multiple sources—not just one.
- Our feeds capture every price update.
- We deliver this high-frequency data in real time using SSL-encrypted transmission to ensure security and integrity.
In short, we provide verified, accurate, and trustworthy tick data you can rely on.
How to Access High-Frequency Data APIs from AllTick
We offer market data APIs for forex, Hong Kong stock CFDs, U.S. stock CFDs, commodities, and cryptocurrencies. These APIs are built for exchanges, developers, quant teams, fintech companies, and institutional users. They’re compatible with all major programming languages and come with detailed documentation. We also offer free trials for integration testing.
If you’re interested in our services, feel free to reach out to our customer support via Telegram.
Code Examples
AllTick’s tick data is wrapped for easy and convenient access. Here’s a look at some example use cases:
1. Retrieve stock candlestick data
import time
import requests
import json
# Extra headers
test_headers = {
'Content-Type':'application/json'
}
'''
github：https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
{"trace":"python_http_test1","data":{"code":"AAPL.US","kline_type":1,"kline_timestamp_end":0,"query_kline_num":2,"adjust_type":0}}
'''
test_url1 = 'https://quote.alltick.io/quote-stock-b-api/kline?token=e945d7d9-9e6e-4721-922a-7251a9d311d0-1678159756806&query=%7B%22trace%22%3A%22python_http_test1%22%2C%22data%22%3A%7B%22code%22%3A%22AAPL.US%22%2C%22kline_type%22%3A1%2C%22kline_timestamp_end%22%3A0%2C%22query_kline_num%22%3A2%2C%22adjust_type%22%3A0%7D%7D'
resp1 = requests.get(url=test_url1, headers=test_headers)
# Decoded text returned by the request
text1 = resp1.text
print(text1)
2. Query stock trade prices
import time
import requests
import json
# Extra headers
test_headers = {
'Content-Type':'application/json'
}
'''
github：https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
{"trace":"python_http_test2","data":{"symbol_list":[{"code": "700.HK"},{"code": "UNH.US"},{"code": "600416.SH"}]}}
'''
test_url1 = 'https://quote.alltick.io/quote-stock-b-api/trade-tick?token=e945d7d9-9e6e-4721-922a-7251a9d311d0-1678159756806&query=%7B%22trace%22%3A%22python_http_test2%22%2C%22data%22%3A%7B%22symbol_list%22%3A%5B%7B%22code%22%3A%20%22700.HK%22%7D%2C%7B%22code%22%3A%20%22UNH.US%22%7D%2C%7B%22code%22%3A%20%22600416.SH%22%7D%5D%7D%7D'
resp1 = requests.get(url=test_url1, headers=test_headers)
# Decoded text returned by the request
text1 = resp1.text
print(text1)
3. Subscribe to real-time stock tick data via WebSocket
import json
import websocket # pip install websocket-client
'''
github：https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
'''
class Feed(object):
def __init__(self):
self.url = 'wss://quote.alltick.io/quote-stock-b-ws-api?token=e945d7d9-9e6e-4721-922a-7251a9d311d0-1678159756806' # websocket url
self.ws = None
def on_open(self, ws):
"""
Callback object which is called at opening websocket.
1 argument:
@ ws: the WebSocketApp object
"""
print('A new WebSocketApp is opened!')
sub_param = {
"cmd_id": 22002,
"seq_id": 123,
"trace":"3baaa938-f92c-4a74-a228-fd49d5e2f8bc-1678419657806",
"data":{
"symbol_list":[
{
"code": "700.HK",
"depth_level": 5,
},
{
"code": "UNH.US",
"depth_level": 5,
},
{
"code": "600416.SH",
"depth_level": 5,
}
]
}
}
sub_str = json.dumps(sub_param)
ws.send(sub_str)
print("depth quote are subscribed!")
def on_data(self, ws, string, type, continue_flag):
"""
4 argument.
The 1st argument is this class object.
The 2nd argument is utf-8 string which we get from the server.
The 3rd argument is data type. ABNF.OPCODE_TEXT or ABNF.OPCODE_BINARY will be came.
The 4th argument is continue flag. If 0, the data continue
"""
def on_message(self, ws, message):
"""
Callback object which is called when received data.
2 arguments:
@ ws: the WebSocketApp object
@ message: utf-8 data received from the server
"""
result = eval(message)
print(result)
def on_error(self, ws, error):
"""
Callback object which is called when got an error.
2 arguments:
@ ws: the WebSocketApp object
@ error: exception object
"""
print(error)
def on_close(self, ws, close_status_code, close_msg):
"""
Callback object which is called when the connection is closed.
2 arguments:
@ ws: the WebSocketApp object
@ close_status_code
@ close_msg
"""
print('The connection is closed!')
def start(self):
self.ws = websocket.WebSocketApp(
self.url,
on_open=self.on_open,
on_message=self.on_message,
on_data=self.on_data,
on_error=self.on_error,
on_close=self.on_close,
)
self.ws.run_forever()
if __name__ == "__main__":
feed = Feed()
feed.start()
4. Access the latest order book quote data
import time
import requests
import json
# Extra headers
test_headers = {
'Content-Type':'application/json'
}
'''
github：https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
{"trace":"python_http_test2","data":{"symbol_list":[{"code": "700.HK"},{"code": "UNH.US"},{"code": "600416.SH"}]}}
'''
test_url1 = 'https://quote.alltick.io/quote-stock-b-api/depth-tick?token=e945d7d9-9e6e-4721-922a-7251a9d311d0-1678159756806&query=%7B%22trace%22%3A%22python_http_test2%22%2C%22data%22%3A%7B%22symbol_list%22%3A%5B%7B%22code%22%3A%20%22700.HK%22%7D%2C%7B%22code%22%3A%20%22UNH.US%22%7D%2C%7B%22code%22%3A%20%22600416.SH%22%7D%5D%7D%7D'
resp1 = requests.get(url=test_url1, headers=test_headers)
# Decoded text returned by the request
text1 = resp1.text
print(text1)
