Understanding tick data for crypto

Hi Franky

• time: usually it would be a UNIX time, meaning the number of seconds from 1970-01-01 00:00:00 till that moment. Some exchanges will use their own timestamp, it could still be reverted back to an understandable time with a different conversion
• Bid Price: the highest bidding price for buying the asset at that timestamp
• Bid Size: the quantity of buying orders placed at Bid Price
• Ask Price/Ask Size: similar to Bid ones, just now it is the lowest selling price
• quantity: the quantity of actual trade that happened (previous ones are placed)
• quoteQty: Bid Size + Ask Size
• IsBuyerMaker: Is the buying order filled by a placed buying order, which means the selling side later filled the pending order, not the reverse order
• isBestMatch: Does the order get filled at the best available price (the lowest ask price for buy orders, the highest bid price for sell orders)? this must hold true for technical and legal reasons (thread)

Most of the definitions could be found in Binance API docs and Bitfinex API docs. Hope this helps.

Best
Louis Szeto

Hi Louis,

Thanks for the reply.  However, I don't think you understood my question.  I'm trying to understand how to convert typical broker trade data into Level 1 data with bid/asks which QC uses.  

I think I found the answer myself.  You need to use third party data providers.  Normal trade data is not good enough as there is not visibility on the order book.

Hey Frankly and all,

Is it possible to run an algorithm with just tick_trade data and not have tick_quote data?

Hi Wayne

We'll have to implement a custom Fill Model such that the orders are filled on the last trading price. You might do so by setting the FillPrice attribute:

# Set the fill models in initialize:
self.Securities["IBM"].SetFillModel(CustomFillModel(self))

# Custom fill model implementation stub
class CustomFillModel(FillModel):
    def __init__(self, algorithm):
        self.algorithm = algorithm

    def MarketFill(self, asset, order):
        fill = super().MarketFill(asset, order)
        fill.FillPrice = self.algorithm.Securities[order.Symbol].Close
        fill.FillQuantity = order.Quantity
        fill.Status = OrderStatus.Filled
        return fill

And for trading decisions, we can filter the Tick resolution data by the TickType attribute:

def OnData(self, data):
    if data.Ticks.ContainsKey(your_symbol):
        ticks = data.Ticks[your_symbol]
        for tick in ticks:
            if tick.TickType == TickType.Trade:
                price = tick.Price
                qty = tick.Quantity
                # Action

Best
Louis