Timeframes

Overview

The XAUUSD Trading Bot analyzes market data across multiple timeframes to provide comprehensive trading signals. Each timeframe offers different perspectives on market trends and patterns.

Supported Timeframes

The bot supports six standard timeframes, from daily to 5-minute charts:

# Timeframes are returned in the market_data dictionary
result = bot.run_analysis()

for timeframe in result['market_data'].keys():
    print(timeframe)
    
# Output:
# D1
# H4
# H1
# M30
# M15
# M5

Timeframe Definitions

D1 - Daily

string

Daily timeframe - Each candle represents one trading dayUse cases:

Long-term trend identification
Major support/resistance levels
Swing trading strategies
Position trading entries/exits

Typical analysis period: 30-90 days of data

H4 - 4-Hour

string

4-Hour timeframe - Each candle represents 4 hours of tradingUse cases:

Medium-term trend analysis
Intraday swing trading
Identifying session-based patterns
Confirming daily trends

Typical analysis period: 10-30 days of data

H1 - 1-Hour

string

1-Hour timeframe - Each candle represents 1 hour of tradingUse cases:

Short-term trend analysis
Intraday trading opportunities
Session breakout strategies
Fine-tuning entry/exit points

Typical analysis period: 5-15 days of data

M30 - 30-Minute

M30

string

30-Minute timeframe - Each candle represents 30 minutes of tradingUse cases:

Scalping and day trading
Quick trend reversals
News event trading
Precise entry timing

Typical analysis period: 3-7 days of data

M15 - 15-Minute

M15

string

15-Minute timeframe - Each candle represents 15 minutes of tradingUse cases:

Active day trading
Scalping strategies
Quick profit targets
High-frequency analysis

Typical analysis period: 1-3 days of data

M5 - 5-Minute

string

5-Minute timeframe - Each candle represents 5 minutes of tradingUse cases:

Ultra-short-term scalping
Market microstructure analysis
Very tight stop-losses
Rapid trade execution

Typical analysis period: 1-2 days of data

Multi-Timeframe Analysis

The bot performs analysis across all timeframes simultaneously to provide a comprehensive market view:

Show Top-Down Analysis

The bot uses a top-down approach:

D1 (Daily): Identify the overall trend
H4 (4-Hour): Confirm trend direction and strength
H1 (1-Hour): Find potential reversal zones
M30 (30-Minute): Refine entry opportunities
M15 (15-Minute): Precise entry timing
M5 (5-Minute): Final entry confirmation

Show Confluence Trading

Signals are stronger when multiple timeframes align:

result = bot.run_analysis()

# Check alignment across timeframes
# Higher timeframes = stronger trend
# Lower timeframes = better entry timing

daily_trend = analyze_trend(result['market_data']['D1'])
h4_trend = analyze_trend(result['market_data']['H4'])

if daily_trend == h4_trend == "BULLISH":
    print("Strong bullish alignment - high probability setup")

Displaying Timeframe Data

The Streamlit application displays timeframe data in a grid layout:

import streamlit as st

def display_market_data(data_str, timeframe):
    """Display formatted market data in an expander"""
    with st.expander(f"📊 {timeframe} Market Data", expanded=False):
        lines = data_str.split('\n')
        for line in lines:
            if line.strip():
                st.text(line)

# Display in 2-column grid
st.markdown("### 📈 Market Data by Timeframe")
cols = st.columns(2)

for idx, (tf, data) in enumerate(result['market_data'].items()):
    with cols[idx % 2]:
        display_market_data(data, tf)

Timeframe Data Structure

Each timeframe’s data is returned as a formatted multi-line string:

market_data[timeframe]

string

Formatted market data containing:

Price Data: Open, High, Low, Close (OHLC)
Volume: Trading volume for the period
Technical Indicators: Moving averages, RSI, MACD, etc.
Trend Information: Current trend direction and strength
Support/Resistance: Key price levels

Example Data Format

result = bot.run_analysis()
print(result['market_data']['H1'])

# Example output:
# Timeframe: H1
# Current Price: 2654.32
# High: 2656.18
# Low: 2651.45
# Volume: 15420
# Trend: BULLISH
# RSI: 62.4
# MACD: Positive crossover
# Support: 2650.00
# Resistance: 2660.00

Best Practices

Show Timeframe Selection by Trading Style

Choose timeframes based on your trading approach:

Trading Style	Primary Timeframe	Confirmation Timeframes
Position Trading	D1	H4, H1
Swing Trading	H4, H1	D1, M30
Day Trading	H1, M30	H4, M15
Scalping	M15, M5	M30, H1

Show Avoid Conflicting Signals

When timeframes show conflicting signals:

# Higher timeframe takes precedence
if daily_signal == "BUY" and m5_signal == "SELL":
    # Trust the daily signal more
    # Wait for lower timeframes to align
    print("Wait for M5 to confirm daily trend")

Show Update Frequency

Different timeframes require different update intervals:

D1: Update once per day
H4: Update every 4 hours
H1: Update hourly
M30: Update every 30 minutes
M15: Update every 15 minutes
M5: Update every 5 minutes

The bot’s auto-refresh feature uses 30-minute intervals as a balanced approach.

Code Examples

result = bot.run_analysis()

# Access specific timeframe data
h4_data = result['market_data']['H4']
print(f"4-Hour Chart Data:\n{h4_data}")

Core Components

Configuration

Overview

Supported Timeframes

Timeframe Definitions

D1 - Daily

H4 - 4-Hour

H1 - 1-Hour

M30 - 30-Minute

M15 - 15-Minute

M5 - 5-Minute

Multi-Timeframe Analysis

Displaying Timeframe Data

Timeframe Data Structure

Example Data Format

Best Practices

Code Examples

Build docs developers (and LLMs) love

Core Components

Configuration

​Overview

​Supported Timeframes

​Timeframe Definitions

​D1 - Daily

​H4 - 4-Hour

​H1 - 1-Hour

​M30 - 30-Minute

​M15 - 15-Minute

​M5 - 5-Minute

​Multi-Timeframe Analysis

​Displaying Timeframe Data

​Timeframe Data Structure

​Example Data Format

​Best Practices

​Code Examples

Build docs developers (and LLMs) love

Overview

Supported Timeframes

Timeframe Definitions

D1 - Daily

H4 - 4-Hour

H1 - 1-Hour

M30 - 30-Minute

M15 - 15-Minute

M5 - 5-Minute

Multi-Timeframe Analysis

Displaying Timeframe Data

Timeframe Data Structure

Example Data Format

Best Practices

Code Examples