Component:Multi Timeframe Analysis

Multi-Timeframe Analysis [MTF]

Layer: 1 (Foundation)

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Purpose

"Are all timeframes pointing the same direction?"

Before entering a trade, we need confirmation that 1D, 4H, 1H, 15M, and 5M timeframes agree on direction. This component calculates a weighted alignment score and determines if we have sufficient confluence to trade.

THIS IS THE FOUNDATION. Everything else builds on top of MTF alignment. If timeframes disagree, we don't trade.

Trading Market Principle

"Trade with the trend across multiple timeframes?"

• Higher timeframes are more reliable. They get higher weights because big players operate on higher timeframes & dictate market direction. Lower timeframe traders follow.
• We need agreement, not perfection → 70% alignment is sufficient
• 15-minute confirmation prevents premature entries

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How It Works

Step 1: Analyze Each Timeframe

For each timeframe [1D, 4H, 1H, 15M, 5M]:

• Uses Component:Trend_Analysis_Core to determine direction (up/down/sideways)
• Checks if price is above or below MA21
• Detects if MA is trending or flat

Step 2: Determine Expected Direction

The primary timeframe (default: 1H) sets the expected direction:

• 1H trending UP → expected direction = "up"
• 1H trending DOWN → expected direction = "down"

Step 3: Check Alignment

Each timeframe is marked as "aligned" if its direction matches the expected direction:

aligned = (timeframe_direction == expected_direction)

Step 4: Calculate Weighted Score

Alignment_Score = Σ(Weight_i × Aligned_i) / Σ(Weight_i)

Where:
  Weight_i = hierarchical weight for timeframe i
  Aligned_i = 1 if aligned, 0 if not

Step 5: 15-Minute Confirmation (Confirmation)

checks that 70% of recent 15M bars confirm the direction:

For LONG:  bullish_bars >= min_bars × 0.70
For SHORT: bearish_bars >= min_bars × 0.70

Step 6: Final Decision

is_aligned = (alignment_score >= min_alignment_score) AND (15m_confirmed)

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Weight Formula

Default hierarchical weights formula:

weight(i) = 1.0 - (i / N) × 0.7

Where:
  i = timeframe index (0-based)
  N = total timeframes (5)
  0.7 = scaling factor

Calculated Weights:

Timeframe	Index	Calculation	Weight
1D	0	1.0 - (0/5 × 0.7)	1.000
4H	1	1.0 - (1/5 × 0.7)	0.860
1H	2	1.0 - (2/5 × 0.7)	0.720
15M	3	1.0 - (3/5 × 0.7)	0.580
5M	4	1.0 - (4/5 × 0.7)	0.440

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Example Calculation

Scenario: 1D, 4H, 1H aligned UP. 15M and 5M are DOWN.

Expected direction: UP (from 4H)

Timeframe   Direction   Aligned   Weight   Contribution
─────────────────────────────────────────────────────────
1D          up          ✓         1.000    1.000
4H          up          ✓         0.860    0.860
1H          up          ✓         0.720    0.720
15M         down        ✗         0.580    0.000
5M          down        ✗         0.440    0.000
─────────────────────────────────────────────────────────
                        Total:    3.600    2.580

Alignment Score = 2.580 / 3.600 = 0.717 (71.7%)

With min_alignment_score = 0.70, this PASSES (71.7% >= 70%).

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Configuration

Parameter	Default	Description
min_alignment_score	0.70	Minimum weighted score required (0-1)
require_all_timeframes_aligned	False	If True, ALL timeframes must align
wait_for_15min_alignment	True	Enable 15-minute confirmation
min_15min_confirmation_bars	2	Bars to check for 15M confirmation
primary_ma_period	21	Primary moving average period
secondary_ma_period	200	Secondary moving average period

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Input

Dictionary of market data by timeframe:

{
  TimeframeValue.DAILY: {
    "close": pd.Series,
    "open": pd.Series,
    "high": pd.Series,
    "low": pd.Series,
    "volume": pd.Series,
    "ma21": pd.Series,
    "ma200": pd.Series
  },
  TimeframeValue.FOUR_HOUR: { ... },
  TimeframeValue.ONE_HOUR: { ... },
  TimeframeValue.FIFTEEN_MIN: { ... },
  TimeframeValue.FIVE_MIN: { ... }
}

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Output

TimeframeAnalysisResult:

Field	Type	Description
aligned	bool	Final alignment decision
alignment_score	float	Weighted score (0-1)
primary_direction	Direction	LONG or SHORT
sufficient_alignment	bool	Score >= threshold
fifteen_min_aligned	bool	15M confirmation result
timeframe_results	Dict	Per-timeframe analysis details

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Key Methods

process()

Main entry point. Called for each 5-minute bar.

_generate_default_weights()

Creates hierarchical weights using formula: 1.0 - (i/N × 0.7)

_calculate_weighted_alignment()

Calculates: Σ(Weight × Aligned) / Σ(Weight)

_check_15min_alignment()

Checks if 70% of recent 15M bars confirm direction.

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Dependencies

Upstream:

• data_manager.py - Provides OHLC data per timeframe

Internal:

• trend_analysis_core.py - Per-timeframe trend direction analysis

Downstream:

• market_state_analysis.py - Receives alignment result
• setup_quality_detection.py - Uses alignment score (30% weight)

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