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The Real Reason I Built My Own Trading AutomationI posted a video on my channel in YT, I share the journey that led me to build my own trading automation system.
Like many retail traders, I started with very little understanding of how much execution, discipline, and emotion affect results. What began as curiosity turned into overconfidence, rule-breaking, and a series of costly mistakes across stocks, options, hype trades, and futures.
Eventually, I realized the real problem was not just strategy.
It was the gap between signal and execution.
That realization pushed me to think differently: not just about finding better setups, but about engineering a better process. In this video, I walk through that journey and explain why automation became the logical next step for me.
This channel is about:
trading system design
execution discipline
automation infrastructure
risk-first thinking
quant-minded process improvement
Not signals. Not hype. Just building systems the right way.
If this resonates with you, subscribe for future videos on architecture, execution logic, and trading automation design. Long 
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Education
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Intro to my python-tradingview strategyAfter three years working on multiple trading strategies, I decided to share my experience and my trades. But before going live, I think I need to explain the roadmap I’ve followed so far.
I initially started coding my strategy in PineScript, which is a powerful tool. It allowed me to simply code my ideas and turn them into trading signals using alerts and conditions. I’m sure you’ve already watched dozens of YouTube videos on how to use webhooks and TradingView to send signals to your trading platform. Anyway, I began developing my strategy in Pine and used webhooks to connect to Tradovate. I went live after a few months of testing—which, of course, was my first mistake. In January 2022, my algo went live. I traded on a strategy that was just tuned on seen data, manually flipping parameters to maximize PnL—a purely overfit model—and I went live with real money. Anyway, the first month was positive and I thought I was the best trader in the world. I even told my wife we were going to be rich, like nothing could stop us. But after three months of trading, I lost—obviously. It was a bummer, but I knew where the problem was: lack of proper backtesting.
So that was the moment I moved my code to Python. It took me a couple of weeks to build an end-to-end backtesting framework in Python. I used Backtrader as a backtesting tool, which is awesome. I’ll have some videos soon to explain more about that. Anyway, moving my code to Python gave me the luxury of backtesting and creating rolling walk-forward optimizations, allowing me not only to refine my strategies but also to test them on 5–6 years of historical data.
Long story short, working with Python enabled me to come up with five different strategies for NQ and ES. I plan to share those trades, but before that, I thought I should share my journey first.
Please leave comments and follow my channel. More to come. Education 
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MY PEPE USDT BUY SELL ALERTS EXPERIMENTFolks, I have been toying with this idea for the past week. I use it to send alerts to a secondary dedicated GMAIL address, I then use Python to connect to the account using an APP password. It fetches alerts, and then submits buy sell orders using Binance API. Sharing it as keen to hear your feedback and welcome enhancement ideas. I hope you find it useful :)
Configuration Inputs
Entry and Exit Pivot Lengths: Defines the number of bars used to calculate low pivots for buy signals and high pivots for sell signals. The flexibility in defining different lengths for entry and exit allows the strategy to adapt to varying market conditions.
Volatility Factor: Modulates the atr (average true range) to scale the offset for labels, enhancing visual clarity based on market volatility.
RSI Confirmation for Buy: Incorporates an RSI filter to confirm buy signals, aiming to avoid potential false entries during oversold market conditions.
Trend EMA Length: Establishes a long-term exponential moving average (EMA) to identify the overall market trend, filtering trades to occur only in favorable conditions (uptrend).
Logic and Conditions
Pivot Calculation: Uses pivot points to determine potential entry and exit positions. Entry pivots are based on lows (for buying opportunities) and exit pivots on highs (for selling opportunities).
Uptrend Confirmation: Trades are only executed when the price is above the defined EMA, ensuring that trades are aligned with the primary market trend.
Signal Conditions:
Buy Signal: A buy signal is triggered when the price crosses above an identified entry pivot, the market is in an uptrend, and optionally if the RSI is below a defined oversold threshold, ensuring the entry is at a potentially undervalued point.
Sell Signal: A sell signal is triggered either when the price crosses above an exit pivot or falls below the EMA trend line, indicating a potential peak or a weakening trend.
Visualization and Alerts
Plot Lines and Labels: Plots the entry and exit pivot lines and the EMA trend line with distinct colors for clarity. Labels are dynamically placed to indicate buy and sell points with details on entry price and profit percentage.
Alerts: Configured to send notifications for buy and sell triggers, providing timely trade information.
This strategy provides a robust framework for traders looking for systematic entry and exit points based on technical indicators, adjusted for volatility and trend, with additional confirmation from the RSI to enhance trade accuracy and effectiveness. 
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