# Advanced Orderflow

This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.


## Getting Started

1. **Enable Public Trades:** in your `config.json` file, set the `use_public_trades` option to true under the `exchange` section.
```json
"exchange": {
   ...
   "use_public_trades": true,
}
```
2. **Configure Orderflow Processing:** Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:

- `scale`: This controls the price bin size for the footprint chart.
- `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration.
- `imbalance_volume`: Filters out imbalances with volume below this threshold.
- `imbalance_ratio`: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.

```json
"orderflow": {
    "scale": 0.5, 
    "stacked_imbalance_range": 3, # needs at least this amount of imblance next to each other
    "imbalance_volume": 1, # filters out below
    "imbalance_ratio": 300 # filters out ratio lower than
  },
```

## Downloading Trade Data for Backtesting

To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.

```bash
freqtrade download-data -p BTC/USDT:USDT --timerange 20230101-  --trading-mode futures  --timeframes 5m --dl-trades
```


## Accessing Orderflow Data

Once activated, several new columns become available in your dataframe:
``` python

    dataframe['trades'] # Contains information about each individual trade.
    dataframe['orderflow'] # Represents a footprint chart dataframe (see below)
    dataframe['bid'] # Total bid volume 
    dataframe['ask'] # Total ask volume
    dataframe['delta'] # Difference between ask and bid volume.
    dataframe['min_delta'] # Minimum delta within the candle
    dataframe['max_delta'] # Maximum delta within the candle
    dataframe['total_trades'] # Total number of trades
    dataframe['stacked_imbalances_bid'] # Price level of stacked bid imbalance 
    dataframe['stacked_imbalances_ask'] # Price level of stacked ask imbalance  
```

You can access these columns in your strategy code for further analysis. Here's an example:
``` python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    # Calculating cumulative delta
    dataframe['cum_delta'] = cumulative_delta(dataframe['delta'])
    # Accessing total trades
    total_trades = dataframe['total_trades']
    ...

def cumulative_delta(delta: Series):
    cumdelta = delta.cumsum()
    return cumdelta

```
### Footprint chart (dataframe['orderflow'])

This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The scale parameter in your configuration determines the price bin size for this representation

The `orderflow` dataframe includes columns like:

- `bid_amount`: Total volume bought at each price level.
- `ask_amount`: Total volume sold at each price level.
- `bid`: Number of buy orders at each price level.
- `ask`: Number of sell orders at each price level.
- `delta`: Difference between ask and bid volume at each price level.
- `total_volume`: Total volume (ask amount + bid amount) at each price level.
- `total_trades`: Total number of trades (ask + bid) at each price level.


By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.