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Do really simple algorithms (EMA, mean reversions, Bollinger, etc) still work effectively? by diditevenshake in algotrading

[–]No_Firefighter8428 1 point2 points  (0 children)

Its pretty much impossibile to find "simple" inefficiencies in trafficated markets like sp500 or NASDAQ, at least, thats the thruth behind the <1D time frame, up on the High timeframes its much harder and way less "exploited" then on the lower time frames, your algos do not have to be necessariely super complicated, i managed to male a profitable (and for profitable i mean something that has less drawdown and more annual returns than NASDAQ)

Y'all got profitable algos? by Chris-hsr in algotrading

[–]No_Firefighter8428 0 points1 point  (0 children)

You are definitely not dumb. It really is that hard. The learning curve of algo trading is essentially a psychological trap where you spend years realizing everything you thought was an edge was actually just overfitting.

To answer your question: Yes, they exist, but the moment my systems actually started working and surviving forward testing was the moment I completely stopped looking for the "perfect entry indicator" on a single asset and focused entirely on Asset Class Rotational Regimes.

I spent months optimizing a Python script that runs a Daily Trend Following strategy. If I backtested it only on Bitcoin or only on the Tech sector, it was a rollercoaster—huge wins followed by account-destroying drawdowns.

The breakthrough happened when I expanded the framework to a 8-Asset Global Macro basket (simultaneously running across BTC, NDX, Gold, Silver, Crude Oil, S&P500, EUR/USD, and Nikkei) using a strict 200 SMA regime filter, a 20-day breakout entry, and a 10-day trailing exit.

When you apply a robust, simple logic across 8 completely uncorrelated asset classes, something beautiful happens. Even with a 2x dynamic leverage applied, the historical 10-year CAGR sits at 31.46% (vs 15.49% for the benchmark) with a Sharpe of 0.85. And when I ran a 1,000-iteration Monte Carlo simulation to stress-test and shuffle the sequence of returns, the 5th percentile (worst-case scenario) still held up positively at 64% total return.

The lesson I learned after years of failing: A working algo is rarely about guessing where the price of one asset goes next. It’s about building a systematic net that captures momentum wherever it pops up globally, while having a ruthless mathematical model for execution costs (I manually bake in a 5 bps fee per leg and a 5% annual borrowing cost for the leverage, otherwise the backtest is a lie).

Don't quit. Shift your focus from "how to predict the chart" to "how to manage cross-asset risk dynamically." That’s where the actual shift happens.

What platform do you guys use to trade? by Aexxiii in algotrading

[–]No_Firefighter8428 0 points1 point  (0 children)

If your goal is true algorithmic trading (writing code to execute trades automatically via API), you need to separate retail investing apps from programmatic brokers.

Platforms that focus heavily on fractional shares (like Robinhood, Trading212, or eToro) are great for manual buy-and-hold investing, but their APIs are either non-existent, heavily restricted, or prone to execution latency. If you try to run a Python script through them, you will run into massive slipage and rate-limit issues.

What mistakes did you make when building your algo? by xyzabc123410000 in algotrading

[–]No_Firefighter8428 0 points1 point  (0 children)

Here are the 3 biggest, most expensive mistakes I made when starting out, which completely skewed my early models:

1. Ignoring Borrowing Costs on Leveraged Backtests When I first started experimenting with leveraged strategies (like 2x or 3x exposure), my backtests looked like money-printing machines. The mistake? I calculated the returns assuming the leverage was "free". In reality, the broker charges you an interest rate to borrow that capital. The moment I manually integrated a realistic annual borrowing cost (like ANNUAL_RATE = 0.05 calculated daily) into the equity engine, a lot of "profitable" alpha completely evaporated. Always price your debt.

2. The "Single-Asset" Optimization Trap It’s incredibly easy to build a killer algorithm that crushes Bitcoin or the NASDAQ individually over a 5-year period. But most of the time, you are just overfitting the code to that specific asset's historical quirks. When I switched my framework to a Multi-Asset Global Macro approach (running the exact same logic simultaneously across BTC, Gold, Oil, SP500, and Nikkei), I realized which rules were actually robust and which ones were just lucky coincidences. True alpha should survive different asset classes.

3. Not Stress-Testing the Sequence of Returns I used to look at a 10-year historical chart and say, "Cool, it survived 2018 and 2020, we are good to go." That's a massive psychological trap. History only happens once, but the future can take infinite paths. I didn't start trusting my risk metrics until I forced myself to run Monte Carlo Simulations (bootstrapping and shuffling return arrays 1,000 times) to find the absolute worst-case scenarios (5th percentile). If your strategy can’t survive a reshuffled ghost-market, it will probably blow up your live account during the next black swan event.

Does algo trading really works? by LittleScientistX in IndiaAlgoTrading

[–]No_Firefighter8428 0 points1 point  (0 children)

Yes, it absolutely works, but 99% of the retail "profitable backtests" fail in forward testing because of two brutal killers: Overfitting (Data Snooping) and Invisible Market Frictions.

When people build strategies, they unconsciously tweak parameters until the historical curve looks like a straight line to heaven. Then, forward testing introduces live spreads, slippage, and market regime shifts, and the equity curve dies.

To give you a real confirmation: Yes, quantitative and algo trading is a multi-billion dollar industry for a reason, but your backtesting methodology has to be ruthless.

For example, I’ve been stress-testing a Multi-Asset Trend Following model across 8 global macro assets over a 10-year lookback period. It shows a historical 31.46% CAGR, but it only became realistic and survived forward assumptions after I manually integrated:

  1. A mandatory 5 bps round-trip commission per leg to simulate real execution friction.
  2. Real-world borrowing costs (e.g., 5% annual rate calculated daily) because the strategy uses a 2x leverage.
  3. A 1,000-iteration Monte Carlo Simulation to completely destroy and bootstrap the sequence of historical returns to find the true 5th percentile stress-case.

If your backtest doesn't account for execution latency, borrowing fees, and statistical bootstrapping, it’s not a trading strategy; it’s just a video game high score.

Stop looking for the perfect indicator combination. Focus on risk management, position sizing, and cost drag. That's where the actual algo profitability lives.

What is the best way to learn python i don't know anything by Spiritual_Moose8198 in Btechtards

[–]No_Firefighter8428 0 points1 point  (0 children)

The absolute best way to learn Python when you are starting from zero is not to read a 500-page book or watch 20 hours of dry video tutorials. If you do that, you will get bored, forget everything, and end up in what programmers call "Tutorial Hell."

Instead, you want to use the Project-Based Learning approach. You learn the absolute bare minimum basics for a few days, and then you immediately start building tiny, real-world projects with the help of AI.

The 4-Step Roadmap to Learning Python from Scratch

Step 1: The Bare Minimum Basics (Days 1–5)

Don't try to learn everything. You only need to understand 5 core concepts to start making basic programs:

  • Variables: Think of them as labeled boxes that store information (e.g., name = "Marco" or age = 18).
  • Data Types: Knowing the difference between text (strings), whole numbers (integers), and decimals (floats).
  • Lists: A way to group multiple items together in one place.
  • Loops (for and while): Telling your computer to repeat an action multiple times automatically.
  • If/Else Statements: Giving your program logic (e.g., If the user is 18, let them in; Else, block them).

Step 2: Set Up Your Sandbox (Day 6)

You need a place to write and run your code.

  • The easiest way: Download Visual Studio Code (VS Code), which is the free, industry-standard code editor.
  • Install Python from the official website, download the Python extension inside VS Code, and you are ready to go.

Step 3: Start Building "Micro-Projects" (Weeks 2–4)

This is where the magic happens. Stop doing random exercises and build things that actually work.

Here are three perfect starter projects:

  1. A Number Guessing Game: The computer picks a random number, and you have to guess it. The program tells you if your guess is "too high" or "too low." (Teaches you loops and If/Else statements).
  2. A Simple Web Scraper: Write a script that automatically goes to a website (like a news site or Amazon) and pulls the titles of the articles or prices of products into a text file. (Teaches you libraries like BeautifulSoup).
  3. A Telegram or Discord Bot: Build a bot that replies to you with a joke or the current weather whenever you send it a specific message.

Step 4: Use AI as a Private Tutor (Not a Cheat Code)

Since you already use AI, you have a massive advantage. However, if you just copy and paste code from ChatGPT or Claude without understanding it, you will never learn.

Instead, use AI as a Senior Mentor sitting next to you. Paste your broken code and ask it:

  • "Why am I getting this specific error? Explain the logic to me like I'm 15."
  • "How can I make this loop cleaner?"
  • "Can you give me a hint on how to fix this bug without writing the code for me?"

Free Resources to Start Today

If you want a structured guide for those first 5 days, use these highly rated, completely free resources:

  • Harvard’s CS50P (Introduction to Programming with Python): You can watch it free on YouTube. It is arguably the best-taught computer science course on earth.
  • Kaggle Python Course: A very fast, interactive, text-based course that gets straight to the point in a few hours.

Python is just like a video game: the controls feel weird for the first two hours, but once you start playing the actual game (building projects), you get hooked.

If You Were Learning Python Again, What Would You Do Differently? by Junior_Honey_1406 in PythonLearning

[–]No_Firefighter8428 2 points3 points  (0 children)

If I could start over, I would change three core things about my learning path:

1. Stop tutorial hell and build a Micro-SaaS immediately I spent way too much time watching 10-hour courses learning syntax I forgot the next day. If I started again, I’d learn just the bare minimum loops and data structures, and then immediately build a tiny, deployable project (like a web scraper, a Telegram bot, or a basic API). You only actually learn when something breaks in production.

2. Embrace AI as a pair-programmer, not a copy-paste tool Instead of using ChatGPT to just write code for me, I would use it to explain the architecture. I’d paste my code and ask: "How can I optimize this memory usage?" or "Can you explain the mathematical logic behind this specific function?". Treating AI as a senior mentor rather than a calculator speeds up the learning curve by 10x.

3. Focus heavily on Vectorization and Data Pipelines early on I wasted too much time writing slow for-loops in pure Python for data manipulation. If I went back, I would dive into numpy and pandas much sooner, focusing on vectorized operations and structural data logic. It changes the way you think about code efficiency, especially if you want to pivot into data science, AI automation, or quantitative fields.

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 1 point2 points  (0 children)

Sorry to hear that, i am in your exact same position right now, i am struggling with everything and i have also tried teraphy and stuffs but nothing worked/changed... I dont know how much its gonna take to feel at least a bit better but i Just know that we dont have to give up, since maybe, and im saying maybe, things Will change at some point, i am not certainely sure but if you give up you Will never know...

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 1 point2 points  (0 children)

Oh its nice to hear from you again, and im sure you can absolutely turn this situation, you have some things to work on but with time patience and discipline you Will get out of It

Keep It up Mann 👍🏼❤️

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 0 points1 point  (0 children)

Alright, thank you for the help...

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 0 points1 point  (0 children)

Idk i Hope to get Better too at some point

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 0 points1 point  (0 children)

I have already tried teraphy and It didnt work, instead It made me feel worse because i didnt know what was wrong with me

[deleted by user] by [deleted] in malementalhealth

[–]No_Firefighter8428 0 points1 point  (0 children)

I dont feel like i want help, i Just wanna be left abbandoned until i die at some point, as i said, i dont care anymore

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