AI Trading: The Revolution You Can't IgnoreThe Era of AI Trading Has Arrived And It's Only Getting Started
Forget the movie version of AI glowing red eyes flawlessly predicting every tick. The real story is colder, quieter, and way more powerful:
We are moving from a world where humans look at charts to a world where machines digest every tick, every candle, every flow of data… and feed you the edge you couldn't see on your own.
Right now, as you read this, AI is already sitting inside:
Execution algorithms routing institutional orders across venues
Risk engines stress testing portfolios in milliseconds
News and sentiment scanners parsing thousands of headlines a minute
Retail tools that turn a paragraph of English into working NYSE:PINE Script
This isn't science fiction. This is the baseline. And the baseline is rising.
The traders who survive this decade won't be the ones fighting AI. They'll be the ones partnering with it.
So What Exactly Is "AI Trading"?
At its core, AI trading is simply using algorithms that can learn from data to make parts of the trading process smarter.
That can mean anything from a small script that filters charts for you, all the way up to full stack systems managing billions. The spectrum looks like this:
AI Assisted Analysis You still click the buttons, but AI does the heavy lifting.
Pattern recognition on charts (trend, ranges, breakouts)
Scanning hundreds of symbols for your exact conditions
Sentiment analysis on news and earnings headlines
Idea generation: "Show me all large‑cap stocks breaking out with above‑average volume"
AI‑Generated Signals The machine tells you what it would do; you decide whether to listen.
Multi‑indicator models that output clear long/short/flat signals
Quant models that score each asset from 0-100 based on your rules
Bots that push alerts when high‑probability setups appear
Fully Automated Trading The system trades end‑to‑end while you supervise.
Execution from signal → order → risk control with no manual clicks
Self adjusting position sizing and risk controls
Strategies that re‑train on fresh data as regimes shift
Wherever you are on that spectrum, you're already in the AI game. The question isn't "Will I use AI?" it's "How deeply will I let it into my process?"
How AI Actually "Sees" the Market
Humans see a chart. AI sees a dataset.
Human view:
One instrument at a time
A couple of timeframes
A handful of indicators you like
Heavily filtered through emotion and bias
AI view:
Thousands of symbols at once
Dozens of timeframes and derived features
Years of historical data compressed into patterns
Zero fear, zero FOMO, zero boredom
Feed a model clean data and it can uncover:
Regimes you feel but can't quantify (trend, chop, grind, panic)
Relationships between assets that hold statistically
Behavioral patterns like "late‑day reversals after gap‑up opens"
Execution patterns in the order book around key levels
But here's the crazy part: AI is completely unforgiving about your assumptions.
If the data is noisy, biased, or poorly structured, the model will happily learn the wrong thing and apply it with perfect discipline. "Garbage in, garbage out" gets amplified at machine speed.
The Era We're Entering: Human + AI, Not Human vs AI
Over the next decade, expect three shifts to accelerate:
From Intuition First → Data‑First
Traders will still have hunches, but they'll validate them against hard data.
Instead of "this looks extended", you'll ask the system: "How often do moves like this actually continue?" and get an answer in seconds.
From Single‑Indicator Thinking → Multi‑Signal Models
No more worshiping one magic oscillator.
AI will blend technicals, fundamentals, flows, and sentiment into a unified view.
From Static Systems → Adaptive Systems
Instead of one set of parameters forever, models will adapt as volatility, liquidity, and structure change.
Think of it as a trading playbook that rewrites itself when the game changes.
Technologies like larger language models, specialized chips, and eventually quantum‑accelerated optimization won't magically "solve" markets, but they will make it cheaper and faster to test ideas, build systems, and manage risk.
The edge shifts from "Can I code this?" to "Can I ask the right questions, define the right constraints, and manage the risk around what the models tell me?"
Where You Fit In As a Trader
In the era of AI, your job becomes less about staring at every tick, and more about designing the rules of the game your tools play.
You define what "good" trades look like.
You choose which markets, timeframes, and risks matter.
You decide when a model is behaving, and when it's time to shut it off.
AI gives you:
Speed: scanning what you could never cover alone
Consistency: executing the plan without emotional drift
Feedback: showing you what really works in your own data
You bring:
Context: macro, narrative, and common sense
Values: what risks you refuse to take
Adaptability: knowing when to step back or switch regimes
Put together, that's where the edge lives.
Getting Started in the AI Era (Without Getting Overwhelmed)
You don't need a PhD, a server rack, or a lab full of quants. You can start small and intelligent:
Audit Your Current Process
Where are you slow? (Scanning, journaling, testing?)
Where are you emotional? (Entries, exits, sizing?)
Those are prime targets for AI assistance.
Add One AI Tool at a Time
Maybe it's an AI screener.
Maybe it's an NYSE:PINE Script assistant that helps you code and backtest.
Maybe it's a journaling tool that tags your trades automatically.
Learn to Read the Data Behind the Magic
Look at win rate, drawdown, expectancy.
Compare AI filtered setups to your old ones.
Keep what clearly improves your edge; drop the rest.
Respect the Risks
Over‑fitted models that look perfect on the past.
Black‑box systems you can't explain.
Over‑reliance on automation with no kill switch.
Is AI Going to Take Over Trading Completely?
In some corners of the market, AI and automation already dominate . High‑frequency execution, index rebalancing, options market‑making, these domains are machine territory.
But markets are more than math. They are human fear, greed, regulation, politics, liquidity constraints, structural changes and unexpected shocks. That messy mix is exactly where human oversight still matters.
The most realistic future isn't "AI replaces traders" it's AI replaces undisciplined, unstructured traders who bring nothing but guesses to the table.
Traders who can think in systems, understand risk, and collaborate with machines? They don't get replaced. They get leverage.
Your Turn
Where are you right now in this evolution?
Still fully manual, doing everything by hand?
Using a few AI assisted tools but not trusting them yet?
Already running bots and systematic strategies?
What part of AI trading are you most curious or skeptical about?
And the big question: Do you think the future of trading belongs to AI, or to traders who know how to use it?
Drop your thoughts in the comments this era is just beginning.
Coding
How to develop a simple Buy&Sell strategy using Pine ScriptIn this article, will explain how to develop a simple backtesting for a Buy&Sell trading strategy using Pine Script language and simple moving average (SMA).
Strategy description
The strategy illustrated works on price movements around the 200-period simple moving average (SMA). Open long positions when the price crossing-down and moves below the average. Close position when the price crossing-up and moves above the average. A single trade is opened at a time, using 5% of the total capital.
Behind the code
Now let's try to break down the logic behind the strategy to provide a method for properly organizing the source code. In this specific example, we can identify three main actions:
1) Data extrapolation
2) Researching condition and data filtering
3) Trading execution
1. GENERAL PARAMETERS OF THE STRATEGY
First define the general parameters of the script.
Let's define the name.
"Buy&Sell Strategy Template "
Select whether to show the output on the chart or within a dashboard. In this example will show the output on the chart.
overlay = true
Specify that a percentage of the equity will be used for each trade.
default_qty_type = strategy.percent_of_equity
Specify percentage quantity to be used for each trade. Will be 5%.
default_qty_value = 5
Choose the backtesting currency.
currency = currency.EUR
Choose the capital portfolio amount.
initial_capital = 10000
Let's define percentage commissions.
commission_type = strategy.commission.percent
Let's set the commission at 0.07%.
commission_value = 0.07
Let's define a slippage of 3.
slippage = 3
Calculate data only when the price is closed, for more accurate output.
process_orders_on_close = true
2. DATA EXTRAPOLATION
In this second step we extrapolate data from the historical series. Call the calculation of the simple moving average using close price and 200 period bars.
sma = ta.sma(close, 200)
3. DEFINITION OF TRADING CONDITIONS
Now define the trading conditions.
entry_condition = ta.crossunder(close, sma)
The close condition involves a bullish crossing of the closing price with the average.
exit_condition = ta.crossover(close, sma)
4. TRADING EXECUTION
At this step, our script will execute trades using the conditions described above.
if (entry_condition==true and strategy.opentrades==0)
strategy.entry(id = "Buy", direction = strategy.long, limit = close)
if (exit_condition==true)
strategy.exit(id = "Sell", from_entry = "Buy", limit = close)
5. DESIGN
In this last step will draw the SMA indicator, representing it with a red line.
plot(sma, title = "SMA", color = color.red)
Complete code below.
//@version=6
strategy(
"Buy&Sell Strategy Template ",
overlay = true,
default_qty_type = strategy.percent_of_equity,
default_qty_value = 5,
currency = currency.EUR,
initial_capital = 10000,
commission_type = strategy.commission.percent,
commission_value = 0.07,
slippage = 3,
process_orders_on_close = true
)
sma = ta.sma(close, 200)
entry_condition = ta.crossunder(close, sma)
exit_condition = ta.crossover(close, sma)
if (entry_condition==true and strategy.opentrades==0)
strategy.entry(id = "Buy", direction = strategy.long, limit = close)
if (exit_condition==true)
strategy.exit(id = "Sell", from_entry = "Buy", limit = close)
plot(sma, title = "SMA", color = color.red)
The completed script will display the moving average with open and close trading signals.
IMPORTANT! Remember, this strategy was created for educational purposes only. Not use it in real trading.
Algorithmic vs. Quantitative Trading: Which Path Should You TakeI’ve always wondered why anyone would stick to traditional trading methods when algorithms and mathematical models could do all the heavy lifting.
I started questioning everything:
• Why do so many mentors still swear by discretionary trading when algorithms could handle all the heavy lifting?
• Do they really have solid proof of their “own” success, or is it just talk?
• Or are they keeping things complex and discretionary on purpose, to confuse people and keep them as members longer?
• Why deal with the stress of emotions and decisions when an algorithm can take care of it all?
• Imagine how much further ahead you could be if you stopped wasting time on manual trades and instead focused on market research and developing your own models.
When I first got into trading, I thought Algorithmic Trading and Quantitative Trading were basically the same thing. But as I dug deeper, I realized they’re two completely different worlds.
Algorithmic Trading: It’s simple – you set the rules and the algorithm executes the trades. No more sitting in front of the screen “controlling your emotions” and trying to manage every little detail. Instead, you let the algorithm handle it, based on the rules you’ve set. It frees up your time to focus on other things rather than staring at price charts all day.
But here’s the thing – it’s not perfect. You’ll still need to test the rules to make sure the data and results you’re getting aren’t overfitted or just random.
Quantitative Trading: A whole different level. It’s not just about executing trades; it’s about understanding the data and math behind market movements. You analyze historical price, economic, and political data, using math and machine learning to predict the future. But it can be complex – techniques like Deep Learning can turn it into a serious challenge.
The upside? This is the most reliable way to trade, and it’s exactly what over 80% of hedge funds do. They rely on quant models to minimize risk and to outperform the market.
So, which path should you choose?
Quantitative Trading can feel overwhelming at first, I recommend starting with the basics. Begin with Pine Script coding in TradingView—start building a foundation with simple strategies and indicators. As you grow more confident, start coding your own ideas into rules and refining your approach to eventually automated your trading strategy.
TradingView is a great tool for this, and I’d highly suggest grabbing the Premium plan. This will give you access to more data and features to make your learning journey smoother.
Dive into the Pine Script documentation , and begin bringing your ideas to life.
I promise, the more you focus on this, the better and more independent you’ll become in trading.
Every day, aim to get just 1% better.
To Your success,
Moein
Coding StyleWhen a coder creates something new, mostly that is with his/her own style. This makes them artists in a way I believe :)
While it is nice to develop a specific style, writing things on a certain way can be very important to understand better what is written.
This example shows a style, while it is possibly nice to see, is very hard to decipher...
Here is the same code, written in a different way, making it easier to read.
indicator("ConeCode-Linefill,educational", max_lines_count=500, overlay=true)
color1 = input.color(color.new(color.blue , 35), 'color 1')
color2 = input.color(color.new(color.lime , 35), 'color 2')
color3 = input.color(color.new(color.red , 35), 'color 3')
iFill = input.bool (true,'fill')
s1 = ta.sma (close, 1000)
s2 = ta.sma (close, 200)
l1 = plot (s1 , 'l1')
l2 = plot (s2 , 'l2')
topVal = s1 > s2 ? math.min(s1, s2) : math.max(s1, s2)
botVal = s1 > s2 ? math.max(s1, s2) : math.min(s1, s2)
topCl = iFill ? s1 > s2 ? color3 : color1 : na
botCl = iFill ? s1 > s2 ? color1 : color2 : na
fill(l1, l2, topVal, botVal, topCl, botCl)
█ More information:
Coding style: Pine Script™ v5 User Manual/Writing scripts/Style guide
Script description: How PineCoders Write and Format Script Descriptions
How to crawl history price or options from TD Ameritrade API?I crawl options data from Yahoo Finance but actually, Yahoo returned fake data if I send a thousand requests.
I research and use TD Ameritrade API to get options, both github.com and github.com is a good rating. However, every time I send a request a new token is generated then the TD Ameritrade API team noticed me. I like tdameritrade package than td-ameritrade-python-api because of the handled tokens.
======================
Major changes in the v0.1.0 update to the way tokens are handled.
You will still need the original authentication instructions, but the TDClient now takes the refresh token and client id, not the access token. A new session class handles token expiration and will automatically call a new token as needed.
======================
And I used Redis to keep tokens in 30 minutes, then my project works smoothly.
You have a job similar please reference github.com you can download source code and use (the package I am testing)
I hope this source saves your time.
Easy to get data from cnbc.com, it is freeIt is a note for developer
Sometime, we need stock data to write strategy checking logic by python. Data from cnbc.com is good and it is free. How to get it?
There are 2 steps to do:
Step 1: install library python
pip install cnbcfinance
Step 2: Get history or get quote realtime
# Get history data
from cnbcfinance import get_history_df
data = get_history_df('AAPL', '30m')
# Get quote
from cnbcfinance import get_history_df
data = get_quota('AAPL')
Coding Indicator in Tradingview from InspirationSo today is rest and research Sunday and I wanted to share with folks how I go about creating new strategies and indicators. Hopefully this can teach traders to fish rather than giving them just the fish (of a buy/sell signal!)
I was inspired by watching a Youtube podcast Desiretotrade by Etienne Crane where I saw on his chart arrows indicating where price closed outside of a Bollinger Band followed by price closing back inside. This made me want to test such a price action phenomenon myself and to do that I needed to code it up.
I'm really a hacker when it comes to programming and Pine Script but I began coding from ZERO knowledge and my tricks, which I share, should hopefully help new Tradingview/Pine Script coders. GOOGLE IS YOUR FRIEND!!! It has all the answers if you just type the correct search :)
Unfortunately, I did not realize that Tradingview cuts off videos at 20 minutes. Oops! I got the meat of the indicator and process of using it all recorded and got cut off as I was cleaning up the code to make it more user friendly. I'll know to keep track of time for the next video!
Please let me know if this was helpful to traders... I get motivated by positive vibes to do more teaching!






