### **8. Fibonacci Retracement** - **Purpose:** Identifies support and resistance levels. - **Usage:** - Buy at retracement levels like 38.2%, 50%, or 61.8% during an uptrend. - Sell at the same levels during a downtrend. - **Best for:** Entry and exit points. --- ### **9. Stochastic Oscillator** - **Purpose:** Measures momentum. - **Key Levels:** - Above 80: Overbought (sell signal). - Below 20: Oversold (buy signal). - **Best for:** Reversal trades or confirming RSI signals. --- ### **10. ATR (Average True Range)** - **Purpose:** Measures volatility. - **Usage:** - Helps set stop-loss and target levels. - Avoids trades in low-volatility periods. - **Best for:** Risk management. --- ### **Combining Indicators for Better Results** Most successful algo traders combine multiple indicators to avoid false signals. For example: - **Trend Following:** Combine Supertrend with EMA. - **Reversal:** Combine Bollinger Bands with RSI or Stochastic. - **Scalping:** Use VWAP with MACD or RSI. --

### **3. Supertrend** - **Purpose:** A trend-following indicator. - **Usage:** - Buy when price crosses above the Supertrend line. - Sell when price falls below it. - **Best for:** Intraday trading or scalping. ### **4. Bollinger Bands** - **Purpose:** Measures volatility. - **Usage:** - Buy when the price touches the lower band and RSI confirms oversold. - Sell when the price touches the upper band and RSI confirms overbought. - **Best for:** Reversal or range-bound markets. ### **5. MACD (Moving Average Convergence Divergence)** - **Purpose:** Combines trend and momentum analysis. - **Usage:** - Use MACD line crossing above the signal line for buy signals. - MACD histogram divergence for reversals. - **Best for:** Trend confirmation and entry timing. ### **6. VWAP (Volume Weighted Average Price)** - **Purpose:** Combines price and volume to gauge fair value. - **Usage:** - Buy below VWAP in a bullish trend. - Sell above VWAP in a bearish trend. - **Best for:** Intraday trading.

The most successful indicators for algorithmic trading in NSE (National Stock Exchange) stocks depend on the trading strategy being implemented (e.g., scalping, swing trading, or intraday trading). However, some indicators are widely used for their reliability and adaptability. Below are some of the most effective ones for NSE stocks: ### **1. Moving Averages (MA)** - **Types:** Simple Moving Average (SMA), Exponential Moving Average (EMA). - **Usage:** - Identify trends. - Use crossovers (e.g., 50 EMA crossing above 200 EMA as a buy signal). - Smooth out price data for better signal clarity. - **Best for:** Trend-following strategies. ### **2. Relative Strength Index (RSI)** - **Purpose:** Measures momentum and identifies overbought/oversold conditions. - **Key Levels:** - Above 70: Overbought (sell signal). - Below 30: Oversold (buy signal). - **Best for:** Reversal strategies or confirming trends.

As of now, Groww does not provide a public API for placing orders or accessing account information. Their platform is primarily designed for individual investors to manage their investments through the Groww website and mobile application. For developers interested in integrating investment functionalities into their applications, alternative services offer APIs designed for such purposes. For instance, FintegrationFS provides a dynamic RESTful service that enables businesses to incorporate mutual fund investments, stock trading, and other financial services into their applications.

can any body guide me to get groww app api to place order using python, dotnet / php etc

import pandas as pd import requests from io import StringIO import datetime import urllib3 # Suppress SSL certificate verification warning urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) # Step 1: Fetch the CSV from the URL url = "https://flattrade.s3.ap-south-1.amazonaws.com/scripmaster/Commodity.csv" response = requests.get(url, verify=False) # Added verify=False to bypass SSL verification # Step 2: Read the CSV into a pandas DataFrame csv_data = response.text df = pd.read_csv(StringIO(csv_data)) # Clean any leading/trailing spaces in column names df.columns = df.columns.str.strip() # Step 3: Get this month in 3-letter format (e.g., "DEC" for December) this_month = datetime.datetime.now().strftime('%b').upper() # Step 4: Filter the data based on expiry month and the given strike price (e.g., 6000) strike_price = 6000 # Ensure that the column exists before filtering if 'Strike' in df.columns: filtered_data = df[df['Strike'] == strike_price] filtered_data = filtered_data[filtered_data['Expiry'].str.contains(this_month)] # Step 5: Now extract individual values for the different options (CE and PE for CRUDEOIL and CRUDEOIL MINI) # Extract values for CRUDEOIL (CE and PE) crude_ce_token = filtered_data.loc[filtered_data['Optiontype'] == 'CE', 'Token'].iloc[0] if not filtered_data[filtered_data['Optiontype'] == 'CE'].empty else None crude_ce_trade_symbol = filtered_data.loc[filtered_data['Optiontype'] == 'CE', 'Tradingsymbol'].iloc[0] if not filtered_data[filtered_data['Optiontype'] == 'CE'].empty else None crude_pe_token = filtered_data.loc[filtered_data['Optiontype'] == 'PE', 'Token'].iloc[0] if not filtered_data[filtered_data['Optiontype'] == 'PE'].empty else None crude_pe_trade_symbol = filtered_data.loc[filtered_data['Optiontype'] == 'PE', 'Tradingsymbol'].iloc[0] if not filtered_data[filtered_data['Optiontype'] == 'PE'].empty else None # Extract values for CRUDEOIL MINI (CE and PE) crude_mini_ce_token = filtered_data.loc[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'CE'), 'Token'].iloc[0] if not filtered_data[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'CE')].empty else None crude_mini_ce_trade_symbol = filtered_data.loc[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'CE'), 'Tradingsymbol'].iloc[0] if not filtered_data[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'CE')].empty else None crude_mini_pe_token = filtered_data.loc[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'PE'), 'Token'].iloc[0] if not filtered_data[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'PE')].empty else None crude_mini_pe_trade_symbol = filtered_data.loc[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'PE'), 'Tradingsymbol'].iloc[0] if not filtered_data[(filtered_data['Symbol'] == 'CRUDEOILM') & (filtered_data['Optiontype'] == 'PE')].empty else None # Print results for CRUDEOIL and CRUDEOIL MINI print(f"CRUDECETOKEN: {crude_ce_token}") print(f"CRUDECETRADESYMBOL: {crude_ce_trade_symbol}") print(f"CRUDEPETOKEN: {crude_pe_token}") print(f"CRUDEPETRADESYMBOL: {crude_pe_trade_symbol}") print(f"CRUDEMINICETOKEN: {crude_mini_ce_token}") print(f"CRUDEMINICETRADESYMBOL: {crude_mini_ce_trade_symbol}") print(f"CRUDEMINIPETOKEN: {crude_mini_pe_token}") print(f"CRUDEMINIPETRADESYMBOL: {crude_mini_pe_trade_symbol}") # Fetch LTP for the tokens def fetch_ltp(token): # Replace with the actual endpoint and parameters for Flattrade API to fetch LTP ltp_url = f"https://api.flattrade.com/ltp/{token}" # This is just an example URL response = requests.get(ltp_url) if response.status_code == 200: data = response.json() return data.get('ltp', None) else: print(f"Error fetching LTP for token {token}") return None # Fetch LTP for each token if crude_ce_token: ltp_crude_ce = fetch_ltp(crude_ce_token) print(f"LTP for CRUDECETOKEN: {ltp_crude_ce}") if crude_pe_token: ltp_crude_pe = fetch_ltp(crude_pe_token) print(f"LTP for CRUDEPETOKEN: {ltp_crude_pe}") if crude_mini_ce_token: ltp_crude_mini_ce = fetch_ltp(crude_mini_ce_token) print(f"LTP for CRUDEMINICETOKEN: {ltp_crude_mini_ce}") if crude_mini_pe_token: ltp_crude_mini_pe = fetch_ltp(crude_mini_pe_token) print(f"LTP for CRUDEMINIPETOKEN: {ltp_crude_mini_pe}") else: print("Column 'Strike' does not exist in the DataFrame!")

import requests import json # FlatTrade API credentials BASE_URL = "https://piconnect.flattrade.in/PiConnectTP" ACCESS_TOKEN = "3f5d7de21j1bbbb1d853ac4c18d221ab84b1ed79037dbc71d9720xa45a071dd6" # Replace with your current token USER_ID = "XXXXXXX" # Replace with your User ID ACCOUNT_ID = "XXXXXXX" # Replace with your Account ID # Headers for API authentication HEADERS = { "Content-Type": "application/json", } def get_positions(): """Fetch the current positions from FlatTrade""" url = f"{BASE_URL}/PositionBook" jData = { "uid": USER_ID, "actid": ACCOUNT_ID, } payload = f"jData={json.dumps(jData)}&jKey={ACCESS_TOKEN}" response = requests.post(url, headers={"Content-Type": "application/x-www-form-urlencoded"}, data=payload) if response.status_code == 200: try: positions = response.json() if isinstance(positions, list): return positions # Return the list of positions else: print("Unexpected response structure:", positions) return [] except json.JSONDecodeError: print("Failed to decode JSON response:", response.text) return [] else: print("Failed to fetch positions:", response.json()) return [] # Run the script positions = get_positions() if positions: print("Positions:") for position in positions: print(position) else: print("No positions found.")

### **8. GUI Automation** Modules for automating user interface interactions: - **`pyautogui`**: Automate mouse and keyboard actions. - **`keyboard`**: Control keyboard inputs. - **`pynput`**: Monitor and control mouse and keyboard. - **`PyWinAuto`**: Automate interactions with Windows GUI applications. **Use Case**: Automate repetitive desktop tasks or test GUI-based applications. --- ### **9. Scheduling and Task Automation** Modules for scheduling and background automation: - **`schedule`**: Lightweight library for job scheduling. - **`apscheduler`**: Advanced scheduling with cron-like features. - **`time`** and **`datetime`**: Basic time-based automation. **Use Case**: Automate tasks at specific intervals or times (e.g., daily backups). --- ### **10. Network and Communication Automation** Modules for network tasks and communication: - **`paramiko`**: Automate SSH connections and commands. - **`smtplib`**: Automate sending emails. - **`socket`**: Automate network communication. - **`mqtt`**: Automate IoT device communication. **Use Case**: Automate network monitoring, device communication, or email notifications.

### **6. Industrial Automation** Modules for interfacing with hardware and industrial systems: - **`pySerial`**: Communicate with serial devices (e.g., Arduino, microcontrollers). - **`RPi.GPIO`**: Control Raspberry Pi GPIO pins. - **`modbus-tk`**: Implement Modbus protocol for industrial devices. - **`opcua`**: OPC UA protocol for industrial systems communication. **Use Case**: Automate device control, monitor sensors, or build industrial IoT systems. --- ### **7. Machine Learning and AI for Automation** Modules for AI-driven automation: - **`scikit-learn`**: Machine learning for data analysis and prediction. - **`tensorflow`** or **`pytorch`**: Deep learning frameworks. - **`opencv`** (AI-related): Integrate AI for object detection or tracking. - **`NLTK`** and **`spaCy`**: Natural language processing for text-based automation. **Use Case**: Predictive maintenance, automated inspection, or AI-enhanced workflows. ---

### **4. Document Processing** Modules for working with PDFs, Word documents, and more: - **`PyPDF2`**: Read and write PDF files. - **`fpdf`**: Generate PDFs from scratch. - **`python-docx`**: Automate Word document creation or editing. - **`reportlab`**: Advanced PDF generation with custom layouts. **Use Case**: Generate invoices, automate report generation, or process documents. --- ### **5. Mathematical and Engineering Automation** Modules for numerical, scientific, or engineering computations: - **`numpy`**: Numerical computations. - **`scipy`**: Advanced scientific and engineering computations. - **`matplotlib`** and **`seaborn`**: Automate data visualization. - **`sympy`**: Symbolic mathematics and algebra. - **`control`**: Automate control system modeling and simulation. **Use Case**: Automate simulations, data analysis, or mathematical problem-solving.

### **2. Image and Video Processing** Modules for automating image and video-based tasks: - **`OpenCV`**: Image and video processing (e.g., object detection, resizing). - **`Pillow` (PIL)**: Handle and manipulate images. - **`imageio`**: Read and write image or video files. - **`moviepy`**: Automate video editing and processing. **Use Case**: Automate inspection in manufacturing, create time-lapse videos, or process images. --- ### **3. Web Automation** Modules for web-related automation: - **`selenium`**: Automate web browsers (e.g., form submission, scraping). - **`requests`**: Automate HTTP requests (e.g., APIs, web scraping). - **`BeautifulSoup`**: Scrape and parse HTML content from web pages. - **`playwright`**: Modern alternative to Selenium for browser automation. **Use Case**: Web scraping, automated testing, or interacting with online systems.

Computer-Aided Automation (CAA) involves the use of software to automate repetitive or complex tasks, often in industrial, engineering, or general computational workflows. In Python, various modules and libraries can be used to implement CAA systems. Below is a categorized list of Python modules relevant to CAA, along with their typical applications: --- ### **1. File and Data Handling** Modules for automating file and data manipulation: - **`os`**: Interact with the operating system (e.g., file/directory manipulation). - **`shutil`**: Advanced file operations, including moving and copying files. - **`glob`**: Search and locate file paths based on patterns. - **`pandas`**: Data analysis and manipulation (e.g., spreadsheets, databases). - **`openpyxl`** and **`xlrd`**: Work with Excel files for data processing. - **`csv`**: Automate CSV file handling. **Use Case**: Automatically organize files, generate reports, or process large datasets. ---

--- ### **Extensions and Enhancements**: 1. **IoT Integration**: - Use MQTT or HTTP APIs to control the automation remotely. 2. **Camera Integration**: - Add a camera module to detect objects using OpenCV and trigger physical or software actions. 3. **Advanced Automation**: - Use `PyAutoGUI` for software-level RPA tasks like filling web forms or controlling applications. - Combine physical and software automation (e.g., detect an on-screen alert and press a physical button). 4. **Voice Control**: - Integrate with voice recognition libraries like `speech_recognition` to trigger RPA tasks via voice commands. --- ### **Use Cases**: - Automating repetitive office tasks (typing, form filling). - Controlling machinery with button pressing. - Combining AI and RPA (e.g., image-based object detection and physical interaction). - Home automation (e.g., controlling appliances via a physical interface). ---

4. Switch to the application or text editor where you want the typing to happen. --- ### **Physical RPA Task Example: Raspberry Pi Controlling a Servo for Button Pressing** #### **Objective**: Use Raspberry Pi to physically press a button (e.g., on a machine or a keypad). --- #### **Hardware Requirements**: 1. Raspberry Pi. 2. Servo motor (e.g., SG90). 3. Jumper wires and breadboard. 4. External power supply (if needed). --- #### **Circuit Setup**: 1. Connect the servo signal pin to GPIO pin **17** on the Raspberry Pi. 2. Connect the servo's power and ground to the Raspberry Pi (or external supply). 3. Test the servo's connection with a basic movement program. --- #### **Python Code Example**: This script uses the `gpiozero` library to control the servo. ```python from gpiozero import Servo from time import sleep servo = Servo(17) # Connect to GPIO 17 def press_button(): print("Pressing button...") servo.max() # Move servo to "press" position sleep(0.5) # Hold for 0.5 seconds servo.min() # Return to default position sleep(0.5) if __name__ == "__main__": print("Starting RPA task...") for i in range(5): # Repeat 5 times press_button() print("Task completed!") ```

2. **Set Up Virtual Desktop (Optional)**: If automating GUI tasks, use a virtual desktop like VNC to observe the automation process. #### **Python Code Example**: This script simulates typing "Hello, World!" repeatedly into any active text field. ```python import pyautogui import time # Function to perform repetitive typing def automated_typing(): for i in range(5): # Number of repetitions pyautogui.typewrite("Hello, World!") # Type the message pyautogui.press("enter") # Press the Enter key time.sleep(2) # Wait 2 seconds between actions if __name__ == "__main__": print("Starting RPA task in 5 seconds...") time.sleep(5) # Delay to switch to the target window automated_typing() print("Task completed!") ``` --- #### **How to Run**: 1. Open a terminal on the Raspberry Pi. 2. Save the script as `rpa_typing.py`. 3. Run it using: ```bash python3 rpa_typing.py ```

RPA (Robotic Process Automation) Raspberry Pi is more powerful than Arduino and can handle more complex tasks, making it a great choice for creating an **RPA (Robotic Process Automation) system** that involves physical and software-based automation. Below is an example of how you can use Raspberry Pi for an RPA project. ### **Example: Raspberry Pi-Based RPA for Automating Keyboard Typing** #### **Objective**: Simulate a Raspberry Pi pressing keys on a keyboard to automate repetitive typing tasks, such as filling out forms or entering data. #### **Hardware Requirements**: 1. Raspberry Pi (any model, e.g., Raspberry Pi 4). 2. USB keyboard (optional if you simulate via Pi). 3. A monitor for initial setup (or use SSH for headless setup). 4. Python installed (pre-installed in Raspberry Pi OS). #### **Software Requirements**: 1. **Python Libraries**: - `pyautogui`: For automating keyboard/mouse actions. - `time`: For delays between actions. Install with: ```bash pip install pyautogui ```

### **7. WorkFusion RPA Express** - **Description**: Offers a free version with limited features for learning and small-scale automation. - **Key Features**: - Pre-configured bots. - Drag-and-drop bot creation. - Workflow orchestration. - **Use Cases**: Simple repetitive tasks like email parsing or file management. - **Website**: [WorkFusion](https://www.workfusion.com) ### **8. Automai RPA** - **Description**: Free edition for small tasks with an easy-to-use interface. - **Key Features**: - Scriptless automation. - Multi-environment support. - Focus on process automation and application testing. - **Use Cases**: Screen automation, workflow testing, and desktop automation. - **Website**: [Automai](https://www.automai.com) ### **9. PyCaret for Python-Based Automation** - **Description**: While primarily for machine learning workflows, it can be adapted for automation tasks. - **Key Features**: - Python-based scripting. - Combines RPA with predictive analytics. - **Use Cases**: Data processing and AI-driven automation tasks. - **Website**: [PyCaret](https://pycaret.org) ### **10. Taskt** - **Description**: Free and open-source desktop automation tool. - **Key Features**: - Record-and-playback automation. - Simple UI with a focus on Windows desktop apps. - Low learning curve. - **Use Cases**: Automating Windows desktop tasks like file handling or data entry. - **Website**: [Taskt](https://taskt.net)

### **4. OpenRPA** - **Description**: An open-source RPA tool designed for both personal and professional use. - **Key Features**: - Supports Node-RED for complex workflows. - Desktop and web automation. - Scalable and customizable. - **Use Cases**: Web scraping, API integration, and desktop task automation. - **Website**: [OpenRPA](https://openrpa.openiap.io) ### **5. TagUI** - **Description**: Open-source RPA tool focused on simplicity and quick deployment. - **Key Features**: - No programming needed; uses natural-language-like syntax. - Runs on Windows, macOS, and Linux. - Integrates with Python for advanced functionality. - **Use Cases**: Browser automation, screen scraping, and form filling. - **Website**: [TagUI](https://tagui.readthedocs.io) ### **6. Robot Framework** - **Description**: Open-source framework widely used for RPA and test automation. - **Key Features**: - Python-based extensibility. - Rich library of pre-built functionalities. - Strong community support. - **Use Cases**: Test automation, data-driven automation workflows, and web automation. - **Website**: [Robot Framework](https://robotframework.org)

Here’s a list of free RPA tools that you can explore: ### **1. UiPath Community Edition** - **Description**: Offers a feature-rich, free version for small businesses, developers, and learners. - **Key Features**: - Drag-and-drop workflow design. - Extensive activity library. - Built-in AI capabilities for intelligent automation. - **Use Cases**: Data scraping, invoice processing, and desktop automation. - **Website**: [UiPath Community](https://www.uipath.com) ### **2. Automation Anywhere Community Edition** - **Description**: Free version for developers and learners with access to their cloud-based platform. - **Key Features**: - AI-powered bots. - Easy-to-use interface with drag-and-drop functionality. - Integrations with cloud and on-premises apps. - **Use Cases**: Task scheduling, data migration, and automation of business processes. - **Website**: [Automation Anywhere](https://www.automationanywhere.com) ### **3. Microsoft Power Automate (Free Tier)** - **Description**: Part of Microsoft's Power Platform, allows basic automation at no cost. - **Key Features**: - Cloud and desktop automation. - Pre-built templates for workflows. - Seamless integration with Microsoft Office 365. - **Use Cases**: Automating repetitive Office 365 tasks, such as email parsing or data entry. - **Website**: [Power Automate](https://powerautomate.microsoft.com)

#### Steps: 1. Download the TradingView Charting Library. 2. Use a custom server or API as the data source. 3. Implement the `onReady`, `resolveSymbol`, and `getBars` methods in JavaScript for your datafeed. --- ### 4. **Scraping TradingView (Not Recommended)** You can scrape candlestick data, but it **violates TradingView's Terms of Service**. If you proceed, do so responsibly and understand the risks. #### Example (Using Python with Selenium/BeautifulSoup): ```python from selenium import webdriver from bs4 import BeautifulSoup url = "https://www.tradingview.com/symbols/NASDAQ-AAPL/" driver = webdriver.Chrome() # Use the appropriate WebDriver for your browser driver.get(url) html = driver.page_source soup = BeautifulSoup(html, 'html.parser') # Locate candlestick data (you'll need to inspect the DOM structure) print(soup.prettify()) driver.quit() ``` ---