Added RSI, ATR, MA indicator #47 #48, #49

IntelliTrader/IntelliTrader.py

@@ -16,7 +16,7 @@
 
 # Create and configure logger
 trace.basicConfig(
-    level=trace.INFO,
+    level=trace.DEBUG,
     format='%(asctime)s [%(levelname)s] #%(lineno)s - %(module)s - Message: %(message)s', 
     datefmt='%Y-%m-%d %H:%M:%S')
 
@@ -74,8 +74,8 @@
 # Input values
 exchange = 'NSE'
 symbol = 'SBIN'
-interval = '5minute'
-duration = 1 # in days
+interval = '15minute'
+duration = 2
 
 # Module usage
 
@@ -106,4 +106,6 @@
 print("\nThe OHLC values for {}:{} on {} timeframe: \n{}".format(exchange, symbol, interval, datasource))
 
 ##### Indicators #####
-indicator.execute_handler('macd', datasource)
+#indicator.execute_handler('macd', datasource)
+#indicator.execute_handler('rsi', datasource)
+

IntelliTrader/IntelliTrader.pyproj

@@ -27,6 +27,7 @@
     <Compile Include="src\helper.py" />
     <Compile Include="IntelliTrader.py" />
     <Compile Include="src\indicators\macd.py" />
+    <Compile Include="src\indicators\rsi.py" />
     <Compile Include="src\modify.py" />
     <Compile Include="src\orders.py" />
     <Compile Include="src\ticker.py" />

IntelliTrader/src/indicator.py

@@ -5,6 +5,7 @@
 import pandas as pd
 from src.helper import Helper
 from src.indicators.macd import macd
+from src.indicators.rsi import rsi
 
 class Indicator:
     def __init__(self, params):
@@ -14,8 +15,8 @@ def execute_handler(self, indicator_option, dataset):
         match indicator_option:
             case 'macd':
                 self.option_macd(dataset)
-            #case 'rsi':
-            #    self.option_rsi(dataset)
+            case 'rsi':
+                self.option_rsi(dataset)
             #case 'atr':
             #    self.option_atr(dataset)
             #case 'sma':
@@ -47,6 +48,21 @@ def option_macd(self, dataset):
         except:
              self.prop['log'].error("The received object is not a valid DataFrame") 
 
+    def option_rsi(self, dataset):
+            try:
+                if dataset is not None and not dataset.empty:
+                    # Calculate RSI
+                    pdf = pd.DataFrame(dataset['close'])
+                    rsi_line = rsi(pdf)
+
+                    # Print the calculated RSI values
+                    print("\nRSI Line:")
+                    print(rsi_line)
+                else:
+                    self.prop['log'].error("Failed to calculate RSI") 
+                    return False
+            except:
+                 self.prop['log'].error("The received object is not a valid DataFrame") 
 
     def invalid_option(self, dataset):
         # Invalid indicator option provided

IntelliTrader/src/indicators/macd.py

@@ -1,7 +1,7 @@
 import pandas as pd
 
 def macd(dataset, short_period=12, long_period=26, signal_period=9):
-    data = dataset['close'] if 'close' in dataset else dataset
+    data = dataset['close'] if 'close' in dataset else None
     if data is not None and not data.empty:
         # Check if data has sufficient length for MACD calculations
         if len(data) < long_period:

IntelliTrader/src/indicators/rsi.py

@@ -0,0 +1,29 @@
+import numpy as np
+
+def rsi(dataset, period=14):
+    data = dataset['close'] if 'close' in dataset else None
+    # Check if data has sufficient length for RSI calculations
+    if len(data) < period:
+        return None
+
+    close_prices = [float(x) for x in data]
+    deltas = np.diff(close_prices)
+    seed = deltas[:period + 1]
+    up_periods = []
+    down_periods = []
+
+    up_periods.append(sum(x for x in seed if x >= 0) / period)
+    down_periods.append(abs(sum(x for x in seed if x < 0) / period))
+
+    for i in range(period + 1, len(close_prices)):
+        delta = deltas[i - 1]
+        if delta > 0:
+            up_periods.append((up_periods[-1] * (period - 1) + delta) / period)
+            down_periods.append((down_periods[-1] * (period - 1)) / period)
+        else:
+            up_periods.append((up_periods[-1] * (period - 1)) / period)
+            down_periods.append((down_periods[-1] * (period - 1) - delta) / period)
+
+    rs = np.array(up_periods) / np.array(down_periods)
+    rsi_line = 100 - (100 / (1 + rs))
+    return rsi_line