From 20901c833adaa272ce8d9802521188daac13acdd Mon Sep 17 00:00:00 2001
From: Robert Caulk <rob.caulk@gmail.com>
Date: Tue, 27 Dec 2022 10:08:09 +0100
Subject: [PATCH] Improve `purge_old_models` explanation

---
 docs/freqai-parameter-table.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md
index d05ce80f3..72ee1e6b3 100644
--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@@ -15,7 +15,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
 | `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
 | `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
-| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
+| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
 | `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
 | `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.