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

1 by Banzai
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.
These users thanked the author Banzai for the post:
Abdi
Rating: 0.6%

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

3 by Banzai
🔗 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
Python1.png
https://www.python.org/downloads/windows/
Download Python version 3.10.11. 64 bit. Other versions don't work. MT5 is way behind technology.

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https://www.python.org/ftp/python/3.10.11/python-3.10.11-amd64.exe
✅ Step 1 — Install Required Software
1️⃣ Install Python (if not already)
  • Version: Python 3.9 – 3.11
  • ✔️ Check “Add Python to PATH”
Verify:

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python --version
2️⃣ Install MetaTrader5 Python Package

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pip install MetaTrader5 pandas numpy
✅ Step 2 — Prepare MT5 Terminal
In MT5:
  1. Open MT5
  2. Login to your trading account
  3. Go to:

    Code: Select all

    Tools → Options → Expert Advisors
  4. Enable:
    • ✅ Allow algorithmic trading
    • ✅ Allow WebRequest (optional for later)
  5. Keep MT5 OPEN (Python connects to the running terminal)
✅ Step 3 — Test MT5 → Python Connection
Minimal Connection Test

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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

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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

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

4 by Banzai
🟡 Agent 1 — Market Watcher (Core Responsibilities)
Now let’s define what Agent 1 actually computes.
1️⃣ 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"
2️⃣ 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)
3️⃣ 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
4️⃣ 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)

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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:

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{
  "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)

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

5 by Banzai
🟡 Agent 1 — Market Watcher
MACD (4,10,5) + Multi-Timeframe (M5 · M15 · H1)
🧱 Folder Structure (Recommended)
project/
│
├── indicators.py # MACD, EMA, ATR, etc.
├── mt5_data.py # MT5 data fetch helpers
└── agent_market_watcher.py # Agent 1 logic
This separation matters later when you scale.
📈 indicators.py (MACD 4,10,5)

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def ema(series, period):
    return series.ewm(span=period, adjust=False).mean()

def macd(close, fast=4, slow=10, signal=5):
    fast_ema = ema(close, fast)
    slow_ema = ema(close, slow)
    macd_line = fast_ema - slow_ema
    signal_line = ema(macd_line, signal)
    hist = macd_line - signal_line
    return macd_line, signal_line, hist
🔌 mt5_data.py (Reusable MT5 Fetch)

Code: Select all

import MetaTrader5 as mt5
import pandas as pd

def get_candles(symbol, timeframe, bars=500):
    if not mt5.initialize():
        raise RuntimeError("MT5 init failed")

    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, bars)
    mt5.shutdown()

    df = pd.DataFrame(rates)
    df['time'] = pd.to_datetime(df['time'], unit='s')
    return df
🧠 agent_market_watcher.py
(MACD + MTF Logic)

Code: Select all

import MetaTrader5 as mt5
from indicators import macd
from mt5_data import get_candles

SYMBOL = "XAUUSD"

TIMEFRAMES = {
    "M5":  mt5.TIMEFRAME_M5,
    "M15": mt5.TIMEFRAME_M15,
    "H1":  mt5.TIMEFRAME_H1
}

def analyze_tf(tf_name, tf_code):
    df = get_candles(SYMBOL, tf_code, 300)

    df['macd'], df['signal'], df['hist'] = macd(df['close'])

    last_hist = df['hist'].iloc[-1]
    prev_hist = df['hist'].iloc[-2]

    if last_hist > 0 and last_hist > prev_hist:
        trend = "BULLISH"
    elif last_hist < 0 and last_hist < prev_hist:
        trend = "BEARISH"
    else:
        trend = "NEUTRAL"

    return {
        "timeframe": tf_name,
        "macd_hist": round(last_hist, 4),
        "trend": trend
    }

def agent_1_market_state():
    report = {}

    for name, tf in TIMEFRAMES.items():
        report[name] = analyze_tf(name, tf)

    return report

if __name__ == "__main__":
    state = agent_1_market_state()
    for tf, data in state.items():
        print(tf, data)
🧪 Example Output (Real-World)
M5 → BULLISH (momentum rising)
M15 → BULLISH (trend confirmation)
H1 → NEUTRAL (higher TF compression)
Structured form:
{
"M5": {"trend":"BULLISH","macd_hist":0.34},
"M15": {"trend":"BULLISH","macd_hist":0.52},
"H1": {"trend":"NEUTRAL","macd_hist":0.01}
}
🧠 How Agent 1 Thinks (This Is Key)
Timeframe // Role
H1 = Market bias / regime
M15 = Trend validation
M5 = Execution timing

Rule example (later in Agent 2):
Only take M5 longs
if M15 is bullish
and H1 is not bearish
This removes 80% of bad trades.
🔥 Why MACD (4,10,5) Works Well Here
  • Faster response for Gold
  • Less lag than 12-26-9
  • Perfect for momentum alignment, not overfitting

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

6 by Banzai
🟡 Agent 1 — Volatility Filter (ATR)
🧠 Concept (Simple but Powerful)
ATR tells you when to trade, not where.
Regime // Meaning // Action
Compression // Market coiled // Prepare for breakout
Expansion // Momentum phase // Trade allowed
Exhaustion // Volatility spike // Reduce risk / avoid
We will gate MACD signals with this filter.
📐 ATR Logic (MT5-Equivalent)
indicators.py — Add ATR

Code: Select all

import pandas as pd

def atr(high, low, close, period=14):
    prev_close = close.shift(1)

    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low - prev_close).abs()
    ], axis=1).max(axis=1)

    return tr.rolling(period).mean()
🧠 Volatility Regime Classification
Logic
  • Compare current ATR to recent ATR average
  • Classify regime

Code: Select all

def volatility_state(atr_series, lookback=20):
    atr_now = atr_series.iloc[-1]
    atr_avg = atr_series.rolling(lookback).mean().iloc[-1]

    if atr_now < atr_avg * 0.8:
        return "COMPRESSION"
    elif atr_now > atr_avg * 1.2:
        return "EXPANSION"
    else:
        return "NORMAL"
These thresholds are Gold-friendly.
🧠 Upgrade Agent 1 (MTF + MACD + ATR)
agent_market_watcher.py

Code: Select all

from indicators import macd, atr, volatility_state
from mt5_data import get_candles
import MetaTrader5 as mt5

SYMBOL = "XAUUSD"

TIMEFRAMES = {
    "M5":  mt5.TIMEFRAME_M5,
    "M15": mt5.TIMEFRAME_M15,
    "H1":  mt5.TIMEFRAME_H1
}

def analyze_tf(tf_name, tf_code):
    df = get_candles(SYMBOL, tf_code, 400)

    # MACD
    df['macd'], df['signal'], df['hist'] = macd(df['close'], 4, 10, 5)

    # ATR
    df['atr'] = atr(df['high'], df['low'], df['close'], 14)
    vol_state = volatility_state(df['atr'])

    # MACD momentum
    hist_now = df['hist'].iloc[-1]
    hist_prev = df['hist'].iloc[-2]

    if hist_now > 0 and hist_now > hist_prev:
        macd_trend = "BULLISH"
    elif hist_now < 0 and hist_now < hist_prev:
        macd_trend = "BEARISH"
    else:
        macd_trend = "NEUTRAL"

    return {
        "timeframe": tf_name,
        "macd_trend": macd_trend,
        "macd_hist": round(hist_now, 4),
        "atr": round(df['atr'].iloc[-1], 3),
        "volatility": vol_state
    }

def agent_1_market_state():
    return {tf: analyze_tf(tf, code) for tf, code in TIMEFRAMES.items()}

if __name__ == "__main__":
    state = agent_1_market_state()
    for tf, data in state.items():
        print(tf, data)
🧪 Example Output

Code: Select all

{
  "M5":  {
    "macd_trend": "BULLISH",
    "volatility": "EXPANSION"
  },
  "M15": {
    "macd_trend": "BULLISH",
    "volatility": "NORMAL"
  },
  "H1":  {
    "macd_trend": "NEUTRAL",
    "volatility": "COMPRESSION"
  }
}
🧠 How This Improves Trading (Important)
❌ Without ATR Filter
  • MACD fires in dead ranges
  • False signals
  • Chop losses
✅ With ATR Filter
  • Trades only when market moves
  • Compression warns of upcoming breakout
  • Expansion confirms real momentum
This alone can cut bad trades by 40–60%.
🔐 Agent Rule (Used Later)
Trade only if:
  • M5 MACD = direction
  • M15 MACD = same direction
  • M5 ATR = EXPANSION
  • H1 ≠ opposite direction
This is professional signal gating.

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