docstr: Given a pandas data frame with columns -> 'Open', 'High', 'Low', 'Close', 'Volume', extract different technical indicators and returns 
docstr_tok: ["Given","a","pandas","data","frame","with","columns","-",">","'Open","'",",","'High","'",",","'Low","'",",","'Close","'",",","'Volume","'",",","extract","different","technical","indicators","and","returns"]
code: def get_technical_indicators(self, price_data):
      """
      Given a pandas data frame with columns -> 'Open', 'High', 'Low', 'Close', 'Volume', extract different technical indicators and returns
      """
      technical_indicators_dictionary = {}
  
      # RSI
      rsi_history = [5, 10, 15]
      for history in rsi_history:
          rsi = (
              ta.momentum.RSIIndicator(
                  price_data["Close"], n=history, fillna=True
              )
              .rsi()
              .values.tolist()
          )
          slope_rsi, r_value_rsi, p_value_rsi = self.calculate_slope(
              rsi[-self.HISTORY_TO_USE :]
          )
          technical_indicators_dictionary["rsi-" + str(history)] = rsi[
              -self.HISTORY_TO_USE :
          ] + [slope_rsi, r_value_rsi, p_value_rsi]
  
      # Stochastics
      stochastic_history = [5, 10, 15]
      for history in stochastic_history:
          stochs = (
              ta.momentum.StochasticOscillator(
                  price_data["High"],
                  price_data["Low"],
                  price_data["Close"],
                  n=history,
                  d_n=int(history / 3),
                  fillna=True,
              )
              .stoch()
              .values.tolist()
          )
          (
              slope_stoch,
              r_value_stoch,
              p_value_stoch,
          ) = self.calculate_slope(stochs[-self.HISTORY_TO_USE :])
          technical_indicators_dictionary[
              "stochs-" + str(history)
          ] = stochs[-self.HISTORY_TO_USE :] + [
              slope_stoch,
              r_value_stoch,
              p_value_stoch,
          ]
  
      # Accumulation Distribution
      acc_dist = ta.volume.acc_dist_index(
          price_data["High"],
          price_data["Low"],
          price_data["Close"],
          price_data["Volume"],
          fillna=True,
      ).values.tolist()
      acc_dist = acc_dist[-self.HISTORY_TO_USE :]
      (
          slope_acc_dist,
          r_value_acc_dist,
          p_value_acc_dist,
      ) = self.calculate_slope(acc_dist)
      technical_indicators_dictionary["acc_dist"] = [
          slope_acc_dist,
          r_value_acc_dist,
          p_value_acc_dist,
      ]
  
      # Ease of movement
      eom_history = [5, 10, 20]
      for history in eom_history:
          eom = ta.volume.ease_of_movement(
              price_data["High"],
              price_data["Low"],
              price_data["Volume"],
              n=history,
              fillna=True,
          ).values.tolist()
          slope_eom, r_value_eom, p_value_eom = self.calculate_slope(
              eom[-self.HISTORY_TO_USE :]
          )
          technical_indicators_dictionary["eom-" + str(history)] = [
              slope_eom,
              r_value_eom,
              p_value_eom,
          ]
  
      # CCI
      cci_history = [5, 10, 20]
      for history in cci_history:
          cci = ta.trend.cci(
              price_data["High"],
              price_data["Low"],
              price_data["Close"],
              n=history,
              c=0.015,
              fillna=True,
          ).values.tolist()
          slope_cci, r_value_cci, p_value_cci = self.calculate_slope(
              cci[-self.HISTORY_TO_USE :]
          )
          technical_indicators_dictionary["cci-" + str(history)] = cci[
              -self.HISTORY_TO_USE :
          ] + [slope_cci, r_value_cci, p_value_cci]
  
      # Daily log return
      daily_return = ta.others.daily_return(
          price_data["Close"], fillna=True
      ).values.tolist()
      daily_log_return = ta.others.daily_log_return(
          price_data["Close"], fillna=True
      ).values.tolist()
      technical_indicators_dictionary[
          "daily_log_return"
      ] = daily_log_return[-self.HISTORY_TO_USE :]
  
      # Volume difference
      volume_list = price_data["Volume"].values.tolist()
      volume_list = [vol for vol in volume_list if vol != 0]
      volume_returns = [
          volume_list[x] / volume_list[x - 1]
          for x in range(1, len(volume_list))
      ]
      slope_vol, r_value_vol, p_value_vol = self.calculate_slope(
          volume_returns[-self.HISTORY_TO_USE :]
      )
      technical_indicators_dictionary[
          "volume_returns"
      ] = volume_returns[-self.HISTORY_TO_USE :] + [
          slope_vol,
          r_value_vol,
          p_value_vol,
      ]
  
      return technical_indicators_dictionary
  
imports: import os
  import ta
  import sys
  import json
  import math
  import pickle
  import random
  import requests
  import matplotlib
  import collections
  import numpy
  from os import walk
  import pandas
  import yfinance
  import datetime
  from scipy.stats import linregress
  from datetime import datetime, timedelta
  import warnings
label_desc: Use image node_id 3 for calling the TAEngine obj's underlying member method code with example usage: obj.get_technical_indicators(price_data) and returns: technical_indicators_dictionary