Fix some random typos

docs/freqai-configuration.md

@@ -32,6 +32,7 @@
         },
     }
 ```
+
 A full example config is available in `config_examples/config_freqai.example.json`.
 
 ## Building a `FreqAI` strategy
@@ -120,7 +121,7 @@ The `FreqAI` strategy requires including the following lines of code in the stan
 
 ```
 
-Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/tragets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. 
+Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. 
 
 Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
 
@@ -172,10 +173,11 @@ Below are the values you can expect to include/use inside a typical strategy dat
 | `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
 
 ## Setting the `startup_candle_count`
-The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., talib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
+
+The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
 
 !!! Note
-    There are instances where the talib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
+    There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
 
     ```
     2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.

docs/freqai-developers.md

@@ -2,7 +2,7 @@
 
 ## Project architecture
 
-The architechture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc.
+The architecture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc.
 
 The class structure and a detailed algorithmic overview is depicted in the following diagram:
 
@@ -18,7 +18,7 @@ There are a variety of built-in [prediction models](freqai-configuration.md#usin
 
 ## Data handling
 
-`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash recililence by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
+`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
 
 ### File structure
 

docs/freqai-feature-engineering.md

@@ -102,7 +102,7 @@ After having defined the `base features`, the next step is to expand upon them u
 
 ```json
     "freqai": {
-        ...
+        //...
         "feature_parameters" : {
             "include_timeframes": ["5m","15m","4h"],
             "include_corr_pairlist": [
@@ -114,7 +114,7 @@ After having defined the `base features`, the next step is to expand upon them u
             "include_shifted_candles": 2,
             "indicator_periods_candles": [10, 20]
         },
-        ...
+        //...
     }
 ```
 

docs/freqai.md

@@ -47,7 +47,7 @@ An overview of the algorithm, explaining the data processing pipeline and model
 
 **Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy.
 
-**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within your strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.
+**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.
 
 **Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models).
 
@@ -72,7 +72,7 @@ pip install -r requirements-freqai.txt
 
 If you are using docker, a dedicated tag with `FreqAI` dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular `FreqAI` dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
-### Common pitfalls
+## Common pitfalls
 
 `FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
 This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively,