Updated 10 Stock Picks in my Portfolio
Introduction:
I'm excited to share my trading journey on TradingView, where I'll be posting a carefully curated list of 10 stocks every trading day, and sometimes even multiple times a day. Through this endeavor, I aim to foster a dynamic environment where market enthusiasts can stay informed and engaged with real-time insights.
Why 10 Stocks?
The selection of these stocks is driven by a comprehensive analysis that takes into account various factors such as historical performance, price movements, and market trends. My algorithm examines a wide range of data points to identify stocks that show promising potential for growth or strategic value.
The Art of Stock Selection:
The stock tickers that make it to the list go through a rigorous evaluation process. I utilize a combination of quantitative metrics and qualitative insights to ensure that the stocks chosen have a strong foundation for potential profitability. This approach not only factors in financial indicators but also considers broader market dynamics.
Intermittent Updates:
To keep readers well-informed and engaged, I'll be posting these stock tickers intermittently throughout the trading day. This approach allows me to capture shifts in the market and share insights in real-time. By doing so, I aim to provide a valuable resource that helps traders and investors navigate the complexities of the stock market.
Your Feedback Matters:
As I embark on this journey, I wholeheartedly welcome feedback, discussions, and collaborations from fellow traders and investors. Your insights and perspectives are invaluable, and together, we can enhance our understanding of the market and refine our strategies.
Algorithm Overview:
In my script, I've developed an automated trading algorithm that utilizes the Alpaca API to manage a stock portfolio. The algorithm employs a systematic approach, leveraging historical price data and predefined criteria for buying and selling decisions.
Key Steps:
Setting Up:
I import the required libraries, configure Alpaca API credentials, and define the API's base URL for either paper or live trading.
Analyzing Account:
I fetch and display essential account details like equity, available cash, margin maintenance, day trading buying power, account status, and market open status.
Portfolio Evaluation:
I retrieve the existing portfolio positions and calculate their cost, market value, and profit/loss.
Using a DataFrame, I analyze this position data and perform calculations such as the sum of market values and costs.
Portfolio Sizing:
I determine the portfolio size based on a specified percentage of your account equity.
I set a condition to check if the market is open.
Data Retrieval:
I gather the list of stock tickers from the S&P500 index.
I obtain historical price data for these stocks within a defined date range.
Data Analysis and Selection:
I compute price changes, cumulative returns, and rank stocks based on recent performance.
From this analysis, I choose the top-performing stocks for potential inclusion in your portfolio.
Managing Positions:
I evaluate the current positions in your portfolio, decide which to retain, and identify new positions based on the analysis.
I calculate the necessary investment and quantity for each selected stock, considering portfolio sizing.
Order Execution:
I compare the existing portfolio with the new selections to determine which positions need buying or selling.
I place market orders to execute the necessary trades based on the earlier determined quantities.
Monitoring and Adjusting:
I introduce a delay (10 seconds in the code) before querying positions again.
This pause ensures that your portfolio reflects the recent trading activities.
Additional Note:
It's worth mentioning that I sometimes adjust the portfolio daily or multiple times a day, depending on market movements and volatility. I welcome any feedback on this experiment, as it's designed to gauge the interest of market participants. Please keep in mind that this initiative aims to better understand the dynamics of trading. As with any trading strategy, it's crucial to be cautious and thoroughly test the algorithm in a controlled environment before considering live trading. Remember that the success of trading algorithms hinges on various factors, including strategy quality, data accuracy, market conditions, and unforeseen events.
I'm excited to share my trading journey on TradingView, where I'll be posting a carefully curated list of 10 stocks every trading day, and sometimes even multiple times a day. Through this endeavor, I aim to foster a dynamic environment where market enthusiasts can stay informed and engaged with real-time insights.
Why 10 Stocks?
The selection of these stocks is driven by a comprehensive analysis that takes into account various factors such as historical performance, price movements, and market trends. My algorithm examines a wide range of data points to identify stocks that show promising potential for growth or strategic value.
The Art of Stock Selection:
The stock tickers that make it to the list go through a rigorous evaluation process. I utilize a combination of quantitative metrics and qualitative insights to ensure that the stocks chosen have a strong foundation for potential profitability. This approach not only factors in financial indicators but also considers broader market dynamics.
Intermittent Updates:
To keep readers well-informed and engaged, I'll be posting these stock tickers intermittently throughout the trading day. This approach allows me to capture shifts in the market and share insights in real-time. By doing so, I aim to provide a valuable resource that helps traders and investors navigate the complexities of the stock market.
Your Feedback Matters:
As I embark on this journey, I wholeheartedly welcome feedback, discussions, and collaborations from fellow traders and investors. Your insights and perspectives are invaluable, and together, we can enhance our understanding of the market and refine our strategies.
Algorithm Overview:
In my script, I've developed an automated trading algorithm that utilizes the Alpaca API to manage a stock portfolio. The algorithm employs a systematic approach, leveraging historical price data and predefined criteria for buying and selling decisions.
Key Steps:
Setting Up:
I import the required libraries, configure Alpaca API credentials, and define the API's base URL for either paper or live trading.
Analyzing Account:
I fetch and display essential account details like equity, available cash, margin maintenance, day trading buying power, account status, and market open status.
Portfolio Evaluation:
I retrieve the existing portfolio positions and calculate their cost, market value, and profit/loss.
Using a DataFrame, I analyze this position data and perform calculations such as the sum of market values and costs.
Portfolio Sizing:
I determine the portfolio size based on a specified percentage of your account equity.
I set a condition to check if the market is open.
Data Retrieval:
I gather the list of stock tickers from the S&P500 index.
I obtain historical price data for these stocks within a defined date range.
Data Analysis and Selection:
I compute price changes, cumulative returns, and rank stocks based on recent performance.
From this analysis, I choose the top-performing stocks for potential inclusion in your portfolio.
Managing Positions:
I evaluate the current positions in your portfolio, decide which to retain, and identify new positions based on the analysis.
I calculate the necessary investment and quantity for each selected stock, considering portfolio sizing.
Order Execution:
I compare the existing portfolio with the new selections to determine which positions need buying or selling.
I place market orders to execute the necessary trades based on the earlier determined quantities.
Monitoring and Adjusting:
I introduce a delay (10 seconds in the code) before querying positions again.
This pause ensures that your portfolio reflects the recent trading activities.
Additional Note:
It's worth mentioning that I sometimes adjust the portfolio daily or multiple times a day, depending on market movements and volatility. I welcome any feedback on this experiment, as it's designed to gauge the interest of market participants. Please keep in mind that this initiative aims to better understand the dynamics of trading. As with any trading strategy, it's crucial to be cautious and thoroughly test the algorithm in a controlled environment before considering live trading. Remember that the success of trading algorithms hinges on various factors, including strategy quality, data accuracy, market conditions, and unforeseen events.