AetherFlow AI DataBridge v1.05: Full Function Information – Buying and selling Methods – 1 February 2026

AetherFlow AI DataBridge

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AetherFlow AI DataBridge v1.05: Full Function Information

A complete walkthrough of each module and have in AetherFlow AI DataBridge for MetaTrader 5 — your full knowledge mining answer for Machine Studying and AI integration.

Introduction

Constructing efficient Machine Studying fashions for buying and selling requires high-quality, multi-dimensional knowledge. Manually gathering technical indicators, worth motion metrics, and market construction knowledge throughout a number of property and timeframes is extraordinarily time-consuming and error-prone.

AetherFlow AI DataBridge solves this problem by automating the whole knowledge assortment course of. This Knowledgeable Advisor mines complete market knowledge from a number of property concurrently, calculates superior technical indicators, and exports every part to wash CSV information prepared to your AI/ML pipelines. It does NOT execute trades — it’s purely an information mining and export software designed for quantitative analysts, algo builders, and AI researchers.

This information covers each characteristic intimately, serving to you configure the software to your particular knowledge necessities and buying and selling analysis wants.

Half 1: Getting Began

Set up:

• Copy AetherFlow_AI_DataBridge.ex5 to your MQL5/Consultants folder
• Restart MT5 or click on “Refresh” in Navigator panel
• Drag the EA onto any chart
• Allow “Permit Algo Buying and selling” in MT5 settings
• Configure your image checklist and export settings

Essential Observe: This EA doesn’t execute any trades. It solely collects and exports market knowledge for exterior evaluation.

Output Location:

By default, CSV information are saved to the MT5 Frequent Folder. Yow will discover this folder by clicking File → Open Information Folder → MQL5 → Recordsdata, or if UseCommonFolder is enabled, the information go to the terminal’s widespread information listing accessible by all MT5 installations.

Output File	Description
AetherFlow_YYYY-MM-DD.csv	Predominant knowledge export file with all metrics
AetherFlow_AI_Prompt.txt	AI immediate template for knowledge interpretation
AetherFlow_YYYY-MM-DD_snapshot_N.csv	Time Lapse archive information (when enabled)

Half 2: Image Configuration

Multi-Asset Information Mining

AetherFlow can concurrently accumulate knowledge from a number of buying and selling devices. You’ll be able to analyze foreign exchange pairs, commodities, indices, and cryptocurrencies in a single export.

SymbolsList — Enter your required symbols separated by commas. The EA routinely detects and adapts to your dealer’s image naming conference (suffixes like .p, m, +, and so forth.).

UseChartSymbolOnly — Set to true for those who solely need to accumulate knowledge for the chart’s present image. Helpful for targeted single-asset evaluation.

Dealer Image Auto-Detection:

The EA intelligently detects your dealer’s image format. In case your dealer makes use of EURUSD.p or EURUSDm as an alternative of EURUSD, the EA routinely maps commonplace image names to your dealer’s format.

Half 3: Timing Configuration

Export Scheduling

Management how steadily knowledge is collected and exported to CSV information.

ExportIntervalMinutes — Units the interval between knowledge exports in minutes. For instance, setting 15 means knowledge is exported each quarter-hour. Shorter intervals present extra granular knowledge however bigger file sizes.

ExportOnInit — When enabled, the EA instantly exports knowledge when first connected to the chart, providing you with immediate knowledge with out ready for the subsequent scheduled interval.

Half 4: OCM – Omni-Foreign money Matrix (Foreign money Energy)

Foreign money Energy Evaluation

The OCM module calculates relative power for main currencies (USD, EUR, GBP, JPY, AUD, NZD, CAD, CHF) by analyzing their efficiency throughout a number of pairs.

OCM_Period — Variety of bars used for power calculation. Greater values present smoother, extra secure readings; decrease values are extra conscious of current adjustments.

OCM_ScalingFactor — Adjusts the output scale. With normalization enabled, values are scaled to 0-100 vary.

OCM_UseNormalization — When true, normalizes all foreign money power values to a 0-100 scale for simpler comparability and ML mannequin coaching.

Output Columns: The CSV contains power values for every main foreign money (OCM_USD, OCM_EUR, OCM_GBP, and so forth.) plus the power values for base and quote currencies of every analyzed pair.

Half 5: IVC – Intrinsic Velocity Core (Momentum)

Momentum Evaluation

The IVC module measures worth momentum relative to a baseline shifting common, normalized by volatility (ATR).

IVC_MA_Period — Interval for the Linear Weighted Transferring Common (LWMA) baseline. This serves because the reference level for momentum calculation.

IVC_ATR_Period — Interval for ATR calculation utilized in volatility normalization.

IVC_K_Factor — Sensitivity multiplier. Greater values scale back sensitivity; decrease values improve it.

IVC_Smooth_Period — EMA smoothing interval utilized to the ultimate IVC output to cut back noise.

Output Vary: IVC values are clamped between -5.0 and +5.0, the place constructive values point out bullish momentum and detrimental values point out bearish momentum.

Half 6: Quantity and Sentiment Evaluation

Quantity-Based mostly Metrics

This module analyzes tick quantity patterns to establish accumulation, distribution, and quantity anomalies.

EnableVolumeSentiment — Toggle quantity evaluation on/off.

Volume_MA_Period — Interval for calculating common quantity baseline.

Volume_Spike_Threshold — Multiplier to establish quantity spikes. A worth of two.0 means quantity should be 2x the common to be flagged as a spike.

Volume_Trend_Period — Lookback interval for figuring out if quantity is trending up or down.

Volume_Sentiment_Period — Interval for calculating shopping for vs promoting strain sentiment.

Output Columns: Volume_Ratio, Volume_Spike (0/1), Volume_Trend (-1/0/1), Sentiment (-100 to +100).

Half 7: Stochastic and CCI Indicators

Oscillator Evaluation

Commonplace oscillators included for overbought/oversold detection and momentum affirmation.

Stochastic Parameters — Commonplace stochastic oscillator configuration. %Ok and %D values are exported for every image.

CCI Parameters — Commodity Channel Index settings. CCI values assist establish cyclical traits and excessive situations.

Output Columns: Stoch_K, Stoch_D, CCI for every image.

Half 8: Auto Assist/Resistance Detection

Fractal-Based mostly S/R Ranges

Routinely detects important help and resistance ranges utilizing fractal evaluation.

Fractal_Period — Variety of bars on all sides required to verify a fractal level.

SR_Lookback — What number of bars again to seek for S/R ranges.

SR_Max_Levels — Most variety of help and resistance ranges to detect (all sides).

SR_Zone_ATR_Multi — Defines the zone width round every stage as a a number of of ATR.

SR_Min_Touches — Minimal variety of worth touches required to verify a stage.

Output Columns: Support_1, Support_2, Support_3, Resistance_1, Resistance_2, Resistance_3, plus distance-to-level metrics.

Half 9: Bid/Ask Unfold Evaluation

Unfold Monitoring

Captures real-time bid and ask costs individually for unfold evaluation.

When enabled, the CSV contains separate columns for Bid worth, Ask worth, and calculated Unfold. This knowledge is effective for analyzing execution prices and liquidity situations.

Half 10: Volatility Rating Evaluation

Volatility Regime Detection

Classifies present volatility relative to historic norms utilizing percentile rankings.

Volatility_ATR_Period — ATR interval for present volatility measurement.

Volatility_Lookback — Historic bars to check towards for percentile calculation.

Volatility_High_Pct / Volatility_Low_Pct — Threshold percentiles for classifying volatility as Excessive, Regular, or Low.

Output Columns: Volatility_Score (percentile 0-100), Volatility_Regime (Excessive/Regular/Low).

Half 11: Time Lapse Archive Mode

Historic Snapshot Recording

Creates periodic snapshots of all knowledge for constructing historic datasets.

EnableTimeLapse — Prompts time lapse archiving mode.

TimeLapse_Interval — Timeframe interval for snapshots (M1, M5, M15, H1, H4, D1).

TimeLapse_SeparateFiles — When true, creates particular person information for every snapshot; when false, appends to a single file.

TimeLapse_Suffix — Customized suffix added to snapshot filenames for group.

Half 12: SMC Liquidity Evaluation

Sensible Cash Ideas Integration

Identifies swing highs/lows that characterize potential liquidity swimming pools.

SMC_Lookback — Variety of bars to seek for swing factors.

SMC_SwingStrength — Bars required on all sides to verify a swing excessive or low.

Output Columns: Swing_High, Swing_Low, Distance_to_Swing_High, Distance_to_Swing_Low (in pips).

Half 13: Multi-Timeframe Technical Indicators

MTF Information Assortment

Collects indicator values throughout a number of timeframes for every image.

Commonplace Indicator Parameters:

Output Columns: For every enabled timeframe, the CSV contains RSI, MACD (essential/sign/histogram), Bollinger Band place, ADX, Supertrend course, Ichimoku indicators, and pattern course.

Half 14: Pattern Evaluation

Pattern Path Detection

Trend_Lookback — Variety of bars for pattern calculation.

Trend_MA_Method — Transferring common technique (SMA, EMA, SMMA, LWMA).

Output: Pattern course values (-1 = Bearish, 0 = Impartial, 1 = Bullish) for every timeframe.

Half 15: Output Configuration

File Naming and Format

CSV_FilePrefix — Prefix for output information. Date is routinely appended.

Prompt_FileName — Filename for the AI immediate template that helps interpret the information.

UseCommonFolder — Save to MT5 widespread folder (accessible by all terminals) or native knowledge folder.

WriteHeader — Embrace column headers within the first row of CSV information.

CSV_Delimiter — Character separating values (comma, semicolon, tab).

Half 16: File Rotation and Cleanup

Computerized File Administration

EnableDailyRotation — Creates a brand new file every day with date-stamped filename.

MaxDaysToKeep — Routinely deletes information older than this many days. Set to 0 for limitless retention.

AutoCleanupOnStart — Runs cleanup routine when EA is initialized.

Half 17: Configuration Examples

Machine Studying Coaching Setup

Actual-Time AI Feed Setup

Foreign money Energy Analysis Setup

Half 18: Information Column Reference

Core Columns (At all times Included):

Column	Description
Timestamp	Export datetime
Image	Buying and selling instrument
Bid / Ask / Unfold	Present costs and unfold
OCM_(Foreign money)	Foreign money power values
IVC	Momentum worth
RSI_(TF)	RSI per timeframe
MACD_(TF)	MACD values per timeframe
Trend_(TF)	Pattern course per timeframe
Volatility_Score	Percentile rating
Support_N / Resistance_N	Detected S/R ranges
Swing_High / Swing_Low	SMC liquidity ranges

Conclusion

AetherFlow AI DataBridge gives professional-grade knowledge mining capabilities for quantitative merchants and AI researchers:

• Multi-Asset Protection — Concurrently accumulate knowledge from foreign exchange, commodities, indices, and crypto
• Complete Metrics — Foreign money power, momentum, quantity, volatility, and technical indicators
• Multi-Timeframe Evaluation — M5 to D1 knowledge in a single export
• SMC Integration — Swing highs/lows and liquidity pool detection
• AI-Prepared Output — Clear CSV format with constant column construction
• Computerized Administration — Every day rotation and cleanup hold your knowledge organized

The important thing to success is matching your configuration to your analysis targets. Begin with the default settings, confirm knowledge high quality in your ML pipeline, then customise parameters as you refine your fashions.

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