Joe Schr
79 lines
2.5 KiB
Markdown
79 lines
2.5 KiB
Markdown
# Advanced Orderflow
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This page explains some advanced tasks and configuration options that can be performed to use orderflow data by downloading public trade data.
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## Quickstart
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enable using public trades in `config.json`
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```
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"exchange": {
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...
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"use_public_trades": true,
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}
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```
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set orderflow processing configuration in `config.json`:
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```
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"orderflow": {
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"scale": 0.5,
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"stacked_imbalance_range": 3, # needs at least this amount of imblance next to each other
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"imbalance_volume": 1, # filters out below
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"imbalance_ratio": 300 # filters out ratio lower than
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},
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```
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## Downloading data for backtesting
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- use `--dl-trades` to fetch trades for timerange
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For example
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``` bash
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freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades
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```
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## Accessing orderflow data
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Several new columns are available when activated.
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``` python
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dataframe['trades'] # every single trade
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dataframe['orderflow'] # footprint chart: see below
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dataframe['bid'] # bid sum
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dataframe['ask'] # ask sum
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dataframe['delta'] # ask - bid
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dataframe['min_delta'] # minimum delta reached within candle
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dataframe['max_delta'] # maximum delta reached within candle
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dataframe['total_trades'] # amount of trades
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dataframe['stacked_imbalances_bid'] # price level stacked imbalance bid occurred
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dataframe['stacked_imbalances_ask'] # price level stacked imbalance ask occurred
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```
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These can be accessed like this:
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``` python
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# calculating cumulative delta
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dataframe['cum_delta'] = cumulative_delta(dataframe['delta'])
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def cumulative_delta(delta: Series):
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cumdelta = delta.cumsum()
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return cumdelta
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```
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### dataframe['orderflow']
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This includes a dataframe that represents a Footprint chart of the Bid vs Ask type. Footprint charts are a type of candlestick chart that provides additional information, such as trade volume and order flow, in addition to price.
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The scale of the price is set by `orderflow.scale` (see above) and thus binned per price level.
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Following columns are available:
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```python
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orderflow_df['bid_amount'] # how much bids were traded
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orderflow_df['ask_amount'] # how much asks were traded
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orderflow_df['bid'] # how many bids trades
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orderflow_df['ask'] # how many asks trades
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orderflow_df['delta'] # ask amount - bid amount
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orderflow_df['total_volume'] # ask amount + bid amount
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orderflow_df['total_trades'] # ask + bid trades
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```
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