Start writing orderflow documentation

docs/advanced-orderflow.md

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+# Advanced Orderflow
+
+This page explains some advanced tasks and configuration options that can be performed to use orderflow data by downloading public trade data.
+
+
+## Quickstart
+
+- enable using public trades in `config.json`
+```
+"exchange": {
+   ...
+   "use_public_trades": true,
+}
+```
+- set orderflow processing configuration in `config.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 data for backtesting
+
+- use `--dl-trades` to fetch trades for timerange
+
+For example
+``` bash
+freqtrade download-data -p BTC/USDT:USDT --timerange 20230101-  --trading-mode futures  --timeframes 5m --dl-trades
+```
+
+
+## Accessing orderflow data
+
+Several new columns are available when activated.
+``` python
+
+    dataframe['trades']
+    dataframe['orderflow']
+    dataframe['bid']
+    dataframe['ask']
+    dataframe['delta']
+    dataframe['min_delta']
+    dataframe['max_delta']
+    dataframe['total_trades']
+    dataframe['stacked_imbalances_bid']
+    dataframe['stacked_imbalances_ask']
+```
+
+These can be accessed like this:
+``` python
+def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    # calculating cumulative delta
+    dataframe['cum_delta'] = cumulative_delta(dataframe['delta'])
+
+def cumulative_delta(delta: Series):
+    cumdelta = delta.cumsum()
+    return cumdelta
+
+```