Tata Elxsi has joined forces with Wind River to enhance the development of software-defined vehicles (SDVs) by utilizing the Studio Developer platform. Announced on July 23, 2024, this partnership will take advantage of Wind River's cloud-native DevSecOps tools to optimize Tata Elxsi's workflows, improving both efficiency and productivity. Wind River's platform, renowned for its strong edge-to-cloud capabilities, will assist Tata Elxsi in addressing the complex challenges associated with developing cloud-connected SDVs.

The Indian government has proposed a complete exemption of customs duties on 25 critical minerals, including rare earth metals, and a reduction in the basic customs duty (BCD) on two of them. The proposal aims to set the customs duty at zero for key minerals such as copper, gallium, germanium, hafnium, indium, lithium, molybdenum, niobium, nickel, potash, rare earth elements (REE), rhenium, strontium, tantalum, tellurium, tin, tungsten, vanadium, zirconium, selenium, cadmium, and silicon (excluding quartz and silicon dioxide). Currently, the customs duty on these 25 minerals ranges from 5% to 7.5%.

A trailing stop is a dynamic stop-loss order that moves with the price of a security. It helps protect gains by enabling a trade to remain open and continue to profit as long as the price is moving in a favorable direction, while closing the trade if the price changes direction by a specified amount. An Average Trailing Stop (ATS) calculation involves using an average metric, such as the average true range (ATR), to determine the stop distance. Here’s how to calculate an ATR-based trailing stop:
Steps to Calculate Average Trailing Stops Using ATR

1. Calculate the True Range (TR) for each period: The True Range is the greatest of the following:
   • Current high minus the current low
     
   • Absolute value of the current high minus the previous close
     
   • Absolute value of the current low minus the previous close
     
   Formula:
   
   TR=max⁡(High−Low,∣High−Previous Close∣,∣Low−Previous Close∣)\text{TR} = \max(\text{High} - \text{Low}, |\text{High} - \text{Previous Close}|, |\text{Low} - \text{Previous Close}|)
   TR=max(High−Low,∣High−Previous Close∣,∣Low−Previous Close∣)
   
2. Calculate the Average True Range (ATR): The ATR is a moving average of the True Range values over a specified period (e.g., 14 days).
   Formula:
   
   ATR=∑i=1nTRin\text{ATR} = \frac{\sum_{i=1}^{n} \text{TR}_i}{n}
   ATR=n∑i=1nTRi
   where nnn is the number of periods.
   
3. Determine the Trailing Stop Distance: Decide on a multiplier for the ATR (commonly 1.5 or 2 times the ATR). The trailing stop distance will be this multiplier times the ATR.
   Formula:
   
   Trailing Stop Distance=ATR×Multiplier\text{Trailing Stop Distance} = \text{ATR} \times \text{Multiplier}
   Trailing Stop Distance=ATR×Multiplier
   
4. Set the Trailing Stop: For a long position, subtract the trailing stop distance from the highest high since the entry:
   
   Trailing Stop=Highest High Since Entry−Trailing Stop Distance\text{Trailing Stop} = \text{Highest High Since Entry} - \text{Trailing Stop Distance}
   Trailing Stop=Highest High Since Entry−Trailing Stop Distance
   For a short position, add the trailing stop distance to the lowest low since the entry:
   
   Trailing Stop=Lowest Low Since Entry+Trailing Stop Distance\text{Trailing Stop} = \text{Lowest Low Since Entry} + \text{Trailing Stop Distance}
   Trailing Stop=Lowest Low Since Entry+Trailing Stop Distance
   

1. Calculate True Range (TR): Assume you have the following prices:
   • Current high: $105
     
   • Current low: $100
     
   • Previous close: $102
     
   TR=max⁡(105−100,∣105−102∣,∣100−102∣)=max⁡(5,3,2)=5\text{TR} = \max(105 - 100, |105 - 102|, |100 - 102|) = \max(5, 3, 2) = 5
   TR=max(105−100,∣105−102∣,∣100−102∣)=max(5,3,2)=5
   
2. Calculate Average True Range (ATR): Suppose the TR values over the last 14 periods are as follows: 5, 4, 6, 3, 5, 4, 5, 6, 7, 4, 5, 6, 5, 4.
   
   ATR=5+4+6+3+5+4+5+6+7+4+5+6+5+414=6914≈4.93\text{ATR} = \frac{5 + 4 + 6 + 3 + 5 + 4 + 5 + 6 + 7 + 4 + 5 + 6 + 5 + 4}{14} = \frac{69}{14} \approx 4.93
   ATR=145+4+6+3+5+4+5+6+7+4+5+6+5+4=1469≈4.93
   
3. Determine the Trailing Stop Distance: Using a multiplier of 2:
   
   Trailing Stop Distance=4.93×2=9.86\text{Trailing Stop Distance} = 4.93 \times 2 = 9.86
   Trailing Stop Distance=4.93×2=9.86
   
4. Set the Trailing Stop: If the highest high since entry was $110:
   
   Trailing Stop=110−9.86=100.14\text{Trailing Stop} = 110 - 9.86 = 100.14
   Trailing Stop=110−9.86=100.14
   

Tata Consultancy Services (TCS) and Rolls-Royce have taken a significant step forward in their longstanding collaboration by expanding their partnership to focus on sustainability initiatives. This strategic expansion is aimed at leveraging TCS’s cutting-edge technology and innovation capabilities to help Rolls-Royce meet its ambitious environmental goals.

Microsoft Windows users worldwide are currently encountering the Blue Screen of Death (BSOD) error, leading to unexpected system shutdowns or restarts.

affected most of the airlines worldwide

most of the stocks are down because of the issue

Glenmark Pharmaceuticals Secures USFDA Approval for Topiramate Capsules

Glenmark Pharmaceuticals Ltd has secured final approval from the USFDA for its Topiramate Capsules. This medication is recognized as bioequivalent and therapeutically equivalent to Topamax Capsules from Janssen Pharmaceuticals, which are used for treating epilepsy and preventing migraines.

L&T has bagged mega orders for Solar plants in the Middle East. L&T has classified order as a 'Mega' order. The renewable arm of L&T has finalized order with a leading developer in the Middle East, for two Solar PV plants of gigawatt scale. The plants will have a cumulative capacity of 3.5 GW.

Heiken Ashi is a type of candlestick chart that provides a better visual representation of trends in the market by smoothing out price data. The Heiken Ashi (HA) candlesticks are calculated using the following formulas:

1. **HA Close** = (Open + High + Low + Close) / 4
2. **HA Open** = (Previous HA Open + Previous HA Close) / 2
3. **HA High** = Maximum of (High, HA Open, HA Close)
4. **HA Low** = Minimum of (Low, HA Open, HA Close)

To calculate the Heiken Ashi values from 15-minute OHLC (Open, High, Low, Close) data, we'll follow these steps:

1. Start with the first candlestick's Open and Close values to initialize the HA Open and HA Close.
2. For each subsequent candlestick, calculate the HA Close, HA Open, HA High, and HA Low using the formulas provided.

Alice Blue provides different methods to fetch the Last Traded Price (LTP) of financial instruments, including through WebSocket connections and normal GET requests by token. Here are the key differences between these two methods:

### WebSocket LTP Fetching

#### Advantages:
1. **Real-Time Updates**:
  - WebSockets provide real-time updates of market data. Once a WebSocket connection is established, the server pushes updates to the client immediately as they happen.
  - This is ideal for applications that require real-time data, such as trading bots or live dashboards.

2. **Efficiency**:
  - WebSockets are more efficient for streaming data as they maintain a persistent connection, reducing the overhead of repeated HTTP requests.
  - This is particularly useful for high-frequency trading or monitoring multiple instruments simultaneously.

#### Disadvantages:
1. **Complexity**:
  - Implementing and managing WebSocket connections can be more complex compared to simple HTTP requests.
  - It requires handling connection lifecycle events like connection establishment, reconnections, and message parsing.

2. **Resource Usage**:
  - WebSockets can consume more resources on both the client and server sides due to the persistent nature of the connection.

#### Example:
```python
import websocket
import json

def on_message(ws, message):
    data = json.loads(message)
    print(f"LTP for {data['token']}: {data['ltp']}")

def on_error(ws, error):
    print(error)

def on_close(ws):
    print("### closed ###")

def on_open(ws):
    subscribe_message = {
        "action": "subscribe",
        "token": "BANKNIFTY JUL FUT"
    }
    ws.send(json.dumps(subscribe_message))

if __name__ == "__main__":
    websocket_url = "wss://example.aliceblueapi.com/stream"
    ws = websocket.WebSocketApp(websocket_url,
                                on_open=on_open,
                                on_message=on_message,
                                on_error=on_error,
                                on_close=on_close)
    ws.run_forever()
```

### Normal GET LTP Fetching by Token

#### Advantages:
1. **Simplicity**:
  - Using a GET request to fetch the LTP by token is straightforward and easy to implement. It involves making a single HTTP request and receiving a response.
  - This is suitable for less frequent data retrieval or where real-time updates are not critical.

2. **Resource Light**:
  - GET requests are stateless and do not require maintaining a persistent connection, which can be less resource-intensive.

#### Disadvantages:
1. **Latency**:
  - HTTP GET requests can have higher latency compared to WebSocket updates because each request needs to establish a connection, send a request, and wait for a response.
  - This method is not suitable for applications requiring real-time data updates.

2. **Overhead**:
  - Repeated GET requests can create significant overhead, especially if polling frequently for updated data.

#### Example:
```python
import requests

def get_ltp_by_token(token):
    url = f"https://example.aliceblueapi.com/getLTP/{token}"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['ltp']
    else:
        return None

token = "BANKNIFTY JUL FUT"
ltp = get_ltp_by_token(token)
if ltp:
    print(f"LTP for {token}: {ltp}")
else:
    print("Failed to fetch LTP")
```

### Summary

- **WebSocket LTP Fetching**:
  - Best for real-time applications needing continuous updates.
  - More complex to implement and manage.
  - Efficient for high-frequency data streaming.

- **Normal GET LTP Fetching by Token**:
  - Simpler and easier to implement.
  - Suitable for less frequent data retrieval.
  - Higher latency and overhead with frequent requests.

The choice between these two methods depends on the specific requirements of your application, such as the need for real-time updates, ease of implementation, and resource constraints.

let's calculate the total buy and sell charges for 1 lot of options in Alice Blue (15 per order), assuming the flat brokerage charge is ₹15 per order instead of ₹20.

### Example Calculation with ₹15 Brokerage Fee

Let's assume you're trading Nifty options and the premium is ₹100 per lot, with 1 lot consisting of 50 units.

#### Buy Charges

1. **Brokerage Fee**: ₹15 per order
2. **Exchange Transaction Charges**: 0.053% of (₹100 * 50) = 0.053% of ₹5000 = ₹2.65
3. **SEBI Turnover Fees**: 0.0001% of ₹5000 = ₹0.005 (rounded to nearest paisa, often ignored)
4. **GST**: 18% of (₹15 + ₹2.65) = 18% of ₹17.65 = ₹3.18
5. **Stamp Duty**: 0.003% of ₹5000 = ₹0.15

**Total Buy Charges**: ₹15 + ₹2.65 + ₹3.18 + ₹0.15 = ₹20.98

#### Sell Charges

1. **Brokerage Fee**: ₹15 per order
2. **Exchange Transaction Charges**: 0.053% of (₹100 * 50) = ₹2.65
3. **SEBI Turnover Fees**: 0.0001% of ₹5000 = ₹0.005 (rounded to nearest paisa, often ignored)
4. **GST**: 18% of (₹15 + ₹2.65) = 18% of ₹17.65 = ₹3.18
5. **Stamp Duty**: Typically not charged on sell transactions for options in most states.

**Total Sell Charges**: ₹15 + ₹2.65 + ₹3.18 = ₹20.83

### Total Round Trip Charges

Adding both buy and sell charges:

**Total Buy Charges**: ₹20.98 
**Total Sell Charges**: ₹20.83 

**Total Charges for 1 Lot Options Trade**: ₹20.98 + ₹20.83 = ₹41.81

### Summary

For a round trip (buy and sell) of 1 lot of options in Alice Blue with a flat brokerage charge of ₹15 per order, the total charges, including all taxes, are approximately ₹41.81.