• Banzai's [MT5] [Python] Personal AI Trading Co-pilots

Posted: **Thu Jan 15, 2026 7:21 pm**

Personal AI
Trading Co-pilots
version 1.00

I wake up one morning and found out there are at least 2 websites
that use A.I. (Artificial Intelligence) to trade for us.
https://www.quantcrawler.com/ which charges about $9.99/month
https://www.luxalgo.com/pricing/ which charges from $67.99/month to $119.99/month

So I ask A.I. to help us to build our personal AI trading co-pilots on our PC.

What You Will End Up With
On your PC you’ll have:

An AI that watches Gold, Silver, or any Forex pairs and tells you exactly when and how to trade —
and can place the trade for you.

It will:
Watch all charts

Compute MACD(4,10,5), pivots, fibs, Volume Profile

Detect your setups

Calculate stop & lot size

Execute or ask for one-click confirmation

Speak to you

You supervise.
The AI does the watching and clicking.

The Technology Stack (Simple & Stable)
You do not need cloud servers.
You will run:

Part // What
Trading = MT5 (recommended)
Bridge = Python
AI Brain = Local LLM (Mistral, Llama, etc.)
Rules Engine = Python
Execution = MT5 Python API
UI = Web dashboard or simple window
Voice = Windows TTS

Everything runs on your PC.

Why MT5 Is Better For This
MT5 has:

Native Python API

Faster data access

Better multi-symbol handling

Easier trade execution

MT4 can work, but MT5 is cleaner for AI.

How the AI Will See the Market
Every few seconds:
MT5 sends to Python:

Code: Select all

XAUUSD M5 OHLC
RSI(3)
Stoch(5,3,3)
MACD(4,10,5)
ATR
Price vs yesterday high/low
Fibonacci levels
Volume Profile

The AI agents keep a live state:

“Gold is overbought, momentum weakening, near resistance, possible sell forming.”

How Trades Are Executed
When all your rules align:
Agent 4 runs:

Code: Select all

If R:R >= 1.5
And spread acceptable
And no news spike
→ Place trade

It sends the order to MT5 instantly.

Or:

“Gold sell ready. Click CONFIRM.”

One click. No panic.

This Is Not Over-Engineering
This is exactly how professional algo desks work — but simplified for one trader.
You are building:

A personal quant assistant.

🛠 Next Step (Practical)
We don’t jump into AI first.
We start with:
Step 1

Connect MT5 → Python and read:

XAUUSD price

RSI(3)

Stoch(5,3,3)

MACD(4,10,5)

Once that works, everything else stacks on top.

Posted: **Thu Jan 15, 2026 7:22 pm**

Agent 1 — Market Watcher

Continuously reads:

XAUUSD

USDCAD

EURUSD

GBPJPY

or any Forex or Crypto pairs

From MT5 via API.

It tracks:

Trend

Volatility

Key levels

Candle behavior

Posted: **Thu Jan 15, 2026 7:25 pm**

Architecture (Simple & Reliable)

MT5 terminal (logged in)
↓
MetaTrader5 Python API
↓
Python Agent (Market Watcher)
↓
Trend / Volatility / Levels / Candle Logic

Important truth:

MT5 does NOT push data outward

Python pulls data from MT5

This is stable, fast, and widely used in algo trading

Step 1 — Install Required Software
Install Python (if not already)

Version: Python 3.9 – 3.11

Check “Add Python to PATH”

Verify:

Code: Select all

python --version

Install MetaTrader5 Python Package

Code: Select all

pip install MetaTrader5 pandas numpy

Step 2 — Prepare MT5 Terminal
In MT5:

Open MT5
Login to your trading account
Go to:
Code: Select all
```
Tools → Options → Expert Advisors
```
Enable:
- Allow algorithmic trading
- Allow WebRequest (optional for later)
Keep MT5 OPEN (Python connects to the running terminal)

Step 3 — Test MT5 → Python Connection
Minimal Connection Test

Code: Select all

import MetaTrader5 as mt5

# Initialize MT5 connection
if not mt5.initialize():
    print("MT5 initialize failed")
    mt5.shutdown()
else:
    print("MT5 connected successfully")

# Check account info
account = mt5.account_info()
print(account)

mt5.shutdown()

If this prints your account details → you’re connected

Step 4 — Pull XAUUSD Market Data
Fetch Candles (OHLC)
i

Code: Select all

mport MetaTrader5 as mt5
import pandas as pd

mt5.initialize()

SYMBOL = "XAUUSD"
TIMEFRAME = mt5.TIMEFRAME_M5   # 5-minute candles
BARS = 300

rates = mt5.copy_rates_from_pos(SYMBOL, TIMEFRAME, 0, BARS)

df = pd.DataFrame(rates)
df['time'] = pd.to_datetime(df['time'], unit='s')

print(df.tail())

mt5.shutdown()

This gives you:

Open

High

Low

Close

Volume

Time

This is your raw feed for Agent 1

Posted: **Thu Jan 15, 2026 7:27 pm**

Agent 1 — Market Watcher (Core Responsibilities)
Now let’s define what Agent 1 actually computes.
Trend Detection (Simple & Robust)

Code: Select all

df['ema_fast'] = df['close'].ewm(span=20).mean()
df['ema_slow'] = df['close'].ewm(span=50).mean()

if df['ema_fast'].iloc[-1] > df['ema_slow'].iloc[-1]:
    trend = "UPTREND"
elif df['ema_fast'].iloc[-1] < df['ema_slow'].iloc[-1]:
    trend = "DOWNTREND"
else:
    trend = "RANGE"

Volatility (ATR-based)

Code: Select all

df['tr'] = df[['high','low','close']].max(axis=1) - df[['high','low','close']].min(axis=1)
df['atr'] = df['tr'].rolling(14).mean()

volatility = df['atr'].iloc[-1]

Interpretation:

Low ATR → compression

Rising ATR → expansion (breakout conditions)

Key Levels (Swing Logic)

Code: Select all

recent_high = df['high'].rolling(50).max().iloc[-1]
recent_low  = df['low'].rolling(50).min().iloc[-1]

These become:

Resistance

Support

Later we’ll add:

Daily high/low

Yesterday high/low

Pivot / Fib

Volume Profile

Candle Behavior (Decision Context)

Code: Select all

last = df.iloc[-1]

body = abs(last['close'] - last['open'])
range_ = last['high'] - last['low']

if body > range_ * 0.6:
    candle_type = "STRONG MOMENTUM"
elif body < range_ * 0.3:
    candle_type = "INDECISION"
else:
    candle_type = "NORMAL"

Agent 1 — Final Output (Example)

Code: Select all

agent_1_report = {
    "symbol": "XAUUSD",
    "timeframe": "M5",
    "trend": trend,
    "volatility_atr": volatility,
    "support": recent_low,
    "resistance": recent_high,
    "candle_state": candle_type
}

print(agent_1_report)

Example output:

Code: Select all

{
  "symbol": "XAUUSD",
  "trend": "UPTREND",
  "volatility_atr": 1.84,
  "support": 2012.30,
  "resistance": 2027.90,
  "candle_state": "STRONG MOMENTUM"
}

What You Have Now

MT5 → Python live data

Agent 1 foundation

Clean, extendable logic

Ready for Agent 2 (Signal Logic)

Posted: **Thu Jan 15, 2026 7:28 pm**

Agent 2 — Indicator Agent
Computes:

RSI(3)

Stoch(5,3,3)

MACD(4,10,5)

Structure

Fibonacci

Pivots

Volume Profile

It keeps a live state:

“Gold: Overbought + momentum slowing + near resistance”

Posted: **Thu Jan 15, 2026 7:29 pm**

Agent 3 — Setup Detector
This agent checks:

“Do all my trading rules align right now?”

Example:
RSI turning down
Stoch crossing
MACD histogram shrinking
At Fibonacci level

It outputs:

BUY

SELL

WAIT

Posted: **Thu Jan 15, 2026 7:30 pm**

Agent 4 — Risk & Execution
This is the one that saves your hands and eyes.
It:

Calculates lot size

Sets SL & TP

Checks R:R ≥ 1.5

Executes trade automatically

Or pops up a single-click confirmation

No more fast mouse clicks.

Posted: **Fri Jan 16, 2026 5:44 am**

Agent 5 — Voice & Dashboard
It tells you:

“Gold sell setup forming. MACD weakening. RSI rolling over.”

You don’t need to stare.

🖥 What Runs This
All on your Windows PC:

MT5

Python

A local AI model (LLM)

Small database

Dashboard in browser

Your GPU helps run the AI locally.

Posted: **Fri Jan 16, 2026 5:45 am**

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Banzai's [MT5] [Python] Personal AI Trading Co-pilots

Banzai's [MT5] [Python] Personal AI Trading Co-pilots

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