Multi-Timeframe Analysis [MTF]

Layer: 1 (Foundation)

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 donot 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, whipsaws

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 [last 2 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)

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

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%).

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

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: { ... }
}

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

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.

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)