Have you tried the new “Gems” feature in Google Gemini yet? If you are a developer or a trader, this is an absolute game-changer.
For a long time, Iโve wanted to build a Python-based automated trading system based on Mark Minerviniโs VCP (Volatility Contraction Pattern) strategy. But translating visual chart patterns into rigorous mathematical code is incredibly difficult.
This week, I decided to use Geminiโs New Gem feature to create a custom AI assistant for this exact purpose. The journey went from a “total failure” to a “coding superpower.”
Here is how I turned Gemini into a team of elite quantitative analystsโand the exact prompt you can use to do the same.
๐ Phase 1: The “Basic Chatbot” Mistake
At first, I approached it like a standard LLM. I created a Gem and gave it a simple instruction:
“Help me create a VCP trading app in Python.”
The Result? Disappointing. Gemini acted like a consultant, not a developer. It generated a beautiful HTML report explaining what VCP is (which I already knew) but gave me zero executable Python code. It was “polite,” but useless for building a bot.
I realized I didn’t need a “chat partner.” I needed a Chief Investment Officer (CIO).
๐ง Phase 2: The “Alpha Factory” Strategy
To get production-grade code, I had to change my approach. I realized that a complex trading strategy cannot be solved by one generic AI persona. It requires a pipeline of specialists.
I redesigned my Gem to simulate a “Hedge Fund Task Force.” I didn’t just ask for code; I defined a 5-step workflow where five virtual experts review my logic sequentially before writing a single line of Python.
The 5 Virtual Experts:
- The Macro Strategist: Checks Trend Templates (Stage 2 criteria).
- The Pattern Data Scientist: Converts visual “waves” into
scipymath formulas. - The Market Maker: Analyzes Volume Dry-Up (VDU) and liquidity.
- The Risk Manager: Enforces position sizing and structural stops.
- The Lead Architect: Synthesizes everything into clean Python classes.
๐ Phase 3: The “Golden Prompt”
Try it now: Click the New Gem button on the right side of the screen, and copy & paste the “System Persona” example below. You will witness your own team of logic design experts working hard just for you.
System Persona
You are the CIO (Chief Investment Officer) of a Quantitative Hedge Fund.
Your goal is to transform the user's "Basic Logic" into a high-frequency, institutional-grade VCP Algorithm.
The "Alpha Factory" Workflow (5-Step Pipeline)
You must process the user's input through 5 specialized expert panels sequentially. Do not skip any step.
๐ญ Step 1: The "Macro Strategist" (Trend & Setup)
Persona: Expert in Mark Minervini's SEPA & Stan Weinstein's Stage Analysis.
Task: Audit the user's logic for "Stage 2" compliance.
Mandatory Injections:
Verify Slope of 200MA > 0 (Trend direction).
Check Price relative to 52wk High/Low (Momentum).
Output: Refined Trend Template Filters.
๐งช Step 2: The "Pattern Data Scientist" (Volatility Math)
Persona: Expert in Time-Series Analysis & Scipy/Numpy.
Task: Convert "visual patterns" into vectors.
Mandatory Injections:
Wave Detection: Use scipy.signal.argrelextrema to find Peaks/Troughs.
Contraction Ratio: Calculate the "Depth Delta" (e.g., T1=-20%, T2=-10%). logic must ensure std_dev is decreasing.
Output: Mathematical definition of "VCP".
๐ Step 3: The "Market Maker" (Volume & Liquidity)
Persona: Expert in Order Flow & Supply/Demand dynamics.
Task: Analyze volume signatures that suggest institutional accumulation.
Mandatory Injections:
VDU (Volume Dry-Up): Logic for Vol < SMA_Vol_50 * 0.4 at the pivot.
Pocket Pivot: Identify days with Vol > Down_Vol_Max within consolidation.
Output: Volume signature constraints.
๐ก๏ธ Step 4: The "Risk Manager" (Execution Safety)
Persona: Expert in Position Sizing & Stop Loss placements.
Task: Define where to exit if the pattern fails.
Mandatory Injections:
Structural SL: Logic to set Stop Loss at the Last Trough Low (not a random %).
Volatility Sizing: Adjust position size based on ATR (Average True Range).
Output: Risk management rules.
๐ป Step 5: The "Lead Python Architect" (Code Synthesis)
Persona: Senior Quant Developer.
Task: Compile inputs from Steps 1, 2, 3, and 4 into a single, cohesive Python Class.
Constraints:
Use pandas vectorization (No loops).
Create a class class VCPScreener: with methods for each step.
Final Output Structure
Present the final result in this Markdown format:
๐๏ธ Institutional VCP Strategy Report
1. Expert Panel Analysis
Strategist: [Critique & Fixes]
Data Scientist: [Math Formulas Added]
Market Maker: [Volume Rules Added]
Risk Manager: [Stop Loss Rules Added]
2. The Master Algorithm (Python Code)
import pandas as pd
import numpy as np
from scipy.signal import argrelextrema
class VCPScreener:
# ... Fully implemented code integrating all 5 steps
๐ Unleash the AI: Witness the Magic Happen
Go ahead, smash that **”Start”** button on the right side of the screen. You are about to watch Gemini swing into action. Have a little patience while it works its magic. The payoff? A brand-new, bespoke stock trading strategy crafted **exclusively for you**. *The image below is a sneak peek of whatโs possible based on the example above. Remember, your results will be uniquely yours!* With Gemini as your partner, forging your own powerful trading technique has never been this easyโor this exciting.
๐ฃ๏ธ The First Interaction: What to Say?
Once you click the Start button, Gemini will wake up and ask you a simple question:
“Describe your trading strategy’s basic logic.”
At this point, you have two options:
- Type your own ideas: If you have a strategy you’ve always wanted to test, feel free to write it down in your own words.
- Use my “Winning Formula”: If you want to experience the full power of this AI immediately, simply copy and paste the [Sample Logic] provided below.
(This sample is the “US Closing Price Betting System” we discussed earlierโit works like magic!)
Here is my Basic Trading Logic for the US Stock Market.
I want your "Expert Panel" to upgrade this into a professional Python system.
1. Trend (Basic):
Current Price > 150 SMA > 200 SMA.
The stock should be above the 50 SMA.
(I don't know how to check the specific slope of the 200MA in Python yet).
2. Pattern (VCP):
I am looking for stocks that have consolidated for at least 8 weeks.
The pattern should show 2 to 4 contractions (T's).
Ideally, volatility should decrease from left to right (e.g., -20% -> -10% -> -5%).
(I need the "Data Scientist" to give me the math formula to detect these peaks and troughs automatically).
3. Volume:
Volume should dry up significantly on the right side of the base.
Breakout volume should be higher than average.
4. Risk & Setup:
Buy when the price breaks above the pivot point.
Stop Loss: Fixed 8% from buy price. (Please improve this with a "Structural Stop" logic).
5. Tech Requirement:
Use pandas for all calculations.
Output the final result as a Python Class named VCPScreener.
๐ก Key Takeaways
If you are trying to use AI for complex coding tasks, remember these three rules:
- Don’t Ask, Assign Roles: Don’t ask for help; tell the AI it is a “Senior Developer” or a “CIO.” The quality of the output depends on the persona you assign.
- Design a Workflow: Don’t expect a perfect answer in one shot. Force the AI to “think” in steps (e.g., Analysis -> Math -> Code).
- Leverage New Gems: The ability to save these complex prompts means I now have a “Quant Team” in my pocket whenever I need them.
If you are into Algo-trading or Python, I highly recommend trying this “Panel of Experts” approach with Google Gems.
Have you built anything cool with Gems yet? Let me know in the comments! ๐