@@ -19,6 +19,7 @@ pip install pyindicators
1919 * [ Exponential Moving Average (EMA)] ( #exponential-moving-average-ema )
2020* Momentum indicators
2121 * [ Relative Strength Index (RSI)] ( #relative-strength-index-rsi )
22+ * [ Relative Strength Index Wilders method (RSI)] ( #relative-strength-index-rsi )
2223
2324## Indicators
2425
@@ -99,7 +100,32 @@ pd_df = ema(pd_df, source_column="Close", period=200, result_column="RSI_14")
99100pd_df.tail(10 )
100101```
101102
102- ![ EMA] ( https://github.com/coding-kitties/PyIndicators/blob/main/static/images/indicators/rsi.png )
103+ ![ RSI] ( https://github.com/coding-kitties/PyIndicators/blob/main/static/images/indicators/rsi.png )
104+
105+ #### Wilders Relative Strength Index (Wilders RSI)
106+
107+ ``` python
108+ from investing_algorithm_framework import CSVOHLCVMarketDataSource
109+
110+ from pyindicators import wilders_rsi
111+
112+ # For this example the investing algorithm framework is used for dataframe creation,
113+ csv_path = " ./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
114+ data_source = CSVOHLCVMarketDataSource(csv_file_path = csv_path)
115+
116+ pl_df = data_source.get_data()
117+ pd_df = data_source.get_data(pandas = True )
118+
119+ # Calculate SMA for Polars DataFrame
120+ pl_df = wilders_rsi(pl_df, source_column = " Close" , period = 200 , result_column = " RSI_14" )
121+ pl_df.show(10 )
122+
123+ # Calculate SMA for Pandas DataFrame
124+ pd_df = wilders_rsi(pd_df, source_column = " Close" , period = 200 , result_column = " RSI_14" )
125+ pd_df.tail(10 )
126+ ```
127+
128+ ![ RSI] ( https://github.com/coding-kitties/PyIndicators/blob/main/static/images/indicators/wilders_rsi.png )
103129
104130### Indicator helpers
105131
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