AI in Trading: The DeepSeek Story and 4 Ways You Can Get Started

🕵️‍♂️ Inside DeepSeek

AI is no longer just theory; it’s now trading and making real money.

For the first time in history, large language models are managing live portfolios with real capital on real markets.

The event that sparked this shockwave is called the Alpha Arena competition, hosted by Nof1 Labs. Think of it like an Olympic Games for AI in trading.

Six of the world’s top AI model training systems competed in the event, including Grok (X), Claude, and GPT-5, were each handed $10,000 in real trading capital and given one mission: trade live markets, no human interference allowed.

And the most surprising part?

While everyone expected the household names like OpenAI’s models or Grok (from Elon Musk’s XAI) to dominate, DeepSeek quietly climbed to the top of the leaderboard, generating consistent profits, superior Sharpe ratios, and low drawdowns.

By the end of the competition, it had outperformed every other AI model training and even the benchmark indexes.

This left the trading world asking the same question you’re probably thinking right now:

“Does this mean AI is finally better than humans at trading?”

Maybe. Maybe not. But one thing’s certain, AI in trading just entered a new era. And this time, it’s not about backtesting or paper trading; it’s about real money on the line.

Let’s unpack the DeepSeek story first, because behind the headlines, there’s a fascinating mix of genius, discipline, and maybe even a little geopolitics.

⚙️ Decoding the DeepSeek Victory

Is this AI model training a true breakthrough, or a product of clever PR?

Let’s start with what made DeepSeek different.

Perspective 1: The Advantage of “Finance DNA”

Unlike general-purpose AI model training, built for text or conversation, DeepSeek was born and bred for markets.

Its founder, Liang Wenfeng, isn’t a typical AI engineer. Before building DeepSeek, he was already a respected figure in quantitative finance and investment strategy. That experience shaped the way DeepSeek “thinks.”

This is what I like to call Finance DNA — the built-in understanding of market psychology, volatility cycles, and risk management. DeepSeek doesn’t just respond to charts; it interprets them like a seasoned trader.

That’s why it beat other AIs that, while more advanced in language understanding, lacked domain specialization.

So yes, part of DeepSeek’s “magic” might simply be the result of being designed with a trader’s brain from the ground up.

But here’s where it gets interesting…

Perspective 2: The Geopolitical Shadow — Or Just Good PR?

We can’t ignore the timing. The US–China AI rivalry is at its peak. With OpenAI, Anthropic, and Google leading the Western front, China has been under pressure to showcase breakthroughs of its own, and DeepSeek’s victory arrives right on cue.

So is this a genuine technological leap or a strategic narrative?

It’s possible that DeepSeek’s success was amplified by state-backed media and national pride. After all, China has been investing heavily in domestic AI ecosystems and desperately wants to prove it can produce AI model training that compete globally.

Even if the hype was political, the performance wasn’t fake. DeepSeek’s actual trades were live, verifiable, and audited by Alpha Arena. The profits were real.

So whether you view it as a geopolitical flex or a genuine innovation, the takeaway is the same: AI in trading is no longer a laboratory experiment. It’s an economic weapon.

🕶 4 Ways You Can Start Using AI in Trading Today

You don’t need a multi-million-dollar AI model training like DeepSeek to step into the world of AI in trading. In fact, you can start today for free with the tools you already have.

The truth is, AI in trading stocks and crypto isn’t about replacing humans; it’s about amplifying your decision-making power. The same logic that made DeepSeek successful.

Let’s break down how you can make AI your trading co-pilot, using the four proven methods from the The Crypto Fire approach.

Tip 1: The 24/7 Analysis Assistant

The crypto market never sleeps, so keeping up with it takes serious motivation. Here’s a simple technique to build an analysis assistant that saves you time and effort in market research.

Step 1: Set Up Your Core Prompt

Access OpenAI ChatGPT and describe your main trading focus as the foundation.

I am a Crypto trader, I trade Bitcoin and Solana and I'm planning to start trading coming Monday.

What are possible earnings reports, news, key economic data or factors I should keep in mind?

What were the key price driving factors this week and can they influence the price on Monday?

Also give me a basic technical analysis on the daily or weekly timeframe based on what could potentially happen in Bitcoin and Solana.

Step 2: Tailor It to Your Markets

Swap out “Bitcoin and Solana” for the instruments you actually trade (like ETH $ETH.X ( ▲ 5.86% ) , DOGE $DOGE.X ( ▲ 5.7% ) , etc). The more specific you are, the sharper the AI’s insights will be.

Outcome:

Tip 2: Trade analysis

A powerful but often overlooked use of AI is turning it into your personal trading coach. With this approach, ChatGPT becomes a neutral partner that checks your trades against your strategy’s rules, spots weaknesses, and suggests smarter ways to improve your setups.

Let’s set up your AI trading assistant!

Step 1: Describe Your Strategy in Detail

Provide ChatGPT with a clear, comprehensive description of your trading rules. The more detail, the better the AI can assist.

I have a strategy that I use on Bitcoin (BTC/USD). It is specifically for the Paris session (e.g., 2 AM - 5 AM EST). I require that the previous Asia session liquidity (highs or lows) is swept first. Once that liquidity sweep occurs, I look for a bullish or bearish break of structure (BOS) on the 5-minute timeframe that aligns with the higher timeframe trend (e.g., 1-hour or 4-hour). My entry is typically on a retest of the BOS level or a fair value gap, with a risk-reward ratio target of 1:2.

I need your help to spot trades on the chart and tell me if it's a valid trade or not. Additionally, if you can suggest any potential improvements to my strategy based on common practices, please do so.

Outcome:

Step 2: Upload Chart Screenshots

Provide clear chart images.

• Mark your Asia session (e.g., with a blue box).

• Mark your Paris session (e.g., with a green box).

• Ensure relevant price action, potential BOS levels and liquidity sweeps are visible.

Step 3: Ask Specific Questions 

Guide the AI's analysis with precise questions.

The Asia session is marked by the blue box and the Paris session by the green box on this 2-day Bitcoin chart. 
Based on the strategy I provided earlier, do you see any valid trade setups here? 
If yes, please specify the entry point, stop loss and potential 1:2 target. 
If no, explain exactly why it doesn't meet the strategy criteria.

Step 4: Get Detailed Analysis

The AI will walk through your rules systematically.

Tip 3: The Emotional Support System

Trading isn't just about analysis; it's a mental game. This AI method acts as your objective, emotionally detached co-pilot when you're in the heat of a trade.

Step 1: Document Your Current Position 

Provide the AI with clear context.

• Take a screenshot of your active trade showing the entry price, current price action, your stop loss and take profit levels.

• Note your position size and the time you've been in the trade.

Step 2: Express Your Concerns 

Be honest about your emotional state and specific worries.

I have taken a trade based on my London strategy that I shared with you. 
I have marked my entry and stop loss.
Do you think it is correct?
I feel anxious and scared about whether I should hold the trade, I also feel I should exit now and take profits.
What should i do?

Step 3: Get Professional Guidance 

The AI uses your strategy and current market context to give clear, unbiased guidance.

Tip 4: The Custom Indicator Builder

I’ve already created several series on custom indicators and advanced strategies to support trading. You can check Part 1 and Part 2.

This time, I’ll be building a brand-new indicator of my own:

Step 1: Strategy Documentation 

Clearly explain your trading approach and the visual elements you want the indicator to display. The more detail, the better.

Build a Pine Script indicator (not a strategy) for BTC/USD that supports any timeframe (optimized for 5-minute) with the following features. 
Use overlay=true, inputs for session times and timezone and include alertcondition().

1) Asia session box  
- Automatically draw a filled box for the Asia session (default 20:00-01:00 EST).  
- Make start/end times and timezone user-configurable.  
- At the end of the Asia session, draw persistent horizontal lines for the Asia High and Asia Low (extend right).

2) Pre-Paris accumulation window  
- Mark a second window right after Asia (default 01:00-02:00 EST) with a different, semi-transparent color.

3) Paris (EU open) first-hour entry window  
- Highlight the first hour of the Paris/London session (default 02:00-03:00 EST) with a distinct background color.  
- Provide on/off toggles for all highlights.

4) Asia sweep check  
- Define an Asia Low sweep as any candle in or after the Pre-Paris window that makes a low < Asia Low and then closes back above Asia Low.  
- After a sweep occurs, enable signals only during the Paris entry window for that day.

5) Bullish BOS (Break of Structure) during entry window  
- Define BOS as a candle closing above the most recent swing high built from the prior N-bar pivot high (default N = 3 left/right).  
- Only trigger if:
  - Asia Low has been swept (as defined above) and  
  - The candle that closes above the swing high occurs within the Paris entry window.

6) Signals & visuals  
- Plot a green up arrow below the candle when BOS conditions are met.  
- Add alertcondition() with a clear message (e.g., “BTCUSD Bullish BOS after Asia Low sweep (Paris window)”).  
- Inputs to customize: colors, opacity, line style/extension, pivot length N and a checkbox to require the BOS close to also be above the Asia high (optional filter).

7) General requirements  
- Pine Script v6.  
- Clear comments and tidy code (sections for inputs, sessions, levels, conditions, plots).  
- Handle session boundaries across days strongly (use time() with sessions and the chosen timezone).  
- Avoid repainting: use only confirmed swing points and barstate.isconfirmed where relevant.

Outcome:

Step 2: Iterative Development 

Work collaboratively with the AI to build and refine the indicator.

• Test each component visually on your TradingView chart.

• Add complexity gradually (e.g., add the liquidity sweep condition, then the BOS alert).

• Debug any issues as they happen by providing the AI with error messages or descriptions of incorrect behavior.

Step 3: Implementation

• Copy the final Pine Script code generated by the AI.

• Paste it into the TradingView Pine Editor (as a new indicator).

• Save and add it to your chart.

• Test thoroughly on historical data.

• Refine the code further based on its performance and visual accuracy.

🧭 The Future of AI in Trading

DeepSeek’s win marked more than a milestone; it showed that AI in trading is no longer experimental.

It’s profitable.

But this doesn’t mean human traders are done. The real opportunity lies in collaboration.

AI can crunch massive data, track risk, and execute with perfect discipline, but it still lacks context and instinct. The future belongs to traders who learn to guide these systems, not compete with them.

DeepSeek’s success also proves that specialization beats scale; domain-specific intelligence will define the next wave of AI in trading stocks and crypto. So stop chasing the “perfect bot.” Use AI to sharpen your own edge, automate the noise, track your performance, and stay disciplined.

The traders who merge human intuition with machine precision will lead the next era of the market. The real question isn’t whether AI will trade for us, but how well we’ll trade with it.