Add Machine ID to Your License

770,62 SEK

License AnalyticaChart4

AnalyticaChart4
AnalyticaChart4+
Stage 1 (PolarityATI)
Stage 2 (PolarityATI + AnalyticaChart4)
Stage 3 (PolarityATI & AnalyticaChart4+)

Product variants

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Description

MAXIMUM OF 3 TOTAL MACHINE ID's PER LICENSE

This products increases the number of Machine ID's you can use per license. All products come with one for free, then 2 more can be added with this product.

Machine ID Transfers are free.

How to Install

Click here to Watch Installation Video

Questions About L2Azimuth? Schedule a Consultation with an Active L2Azimuth Trader.

Backed by Research from Quantis and Statista Q

Read the whitepaper

Information Glossary

PRODUCT INSTALLATION INFORMATION

Installation is a simple process:
Download and run the L2Azimuth.exe file (May be inside of a .zip)

Open NinjaTrader 8
Select desired workspace
Enable Strategy
Start trading!

WATCH US TRADE THIS STRATEGY LIVE

See L2Azimuth in action during our live trading sessions, where we apply the strategy in real market conditions without any delays. Watch as we detect market manipulation, execute trades, and navigate the markets using the same tools available to you. Join us to experience the transparency of our approach and gain insights into how L2Azimuth can enhance your trading.

Counter-Spoof is the first scanner of its kind to not only detect spoofing, but also generates consistent alpha through countering spoofed orders. By identifying the telltale patterns of fake liquidity (unbalanced quoting, phantom depth, cyclical cancellations), the scanner pinpoints when manipulators are overextended and enters reversal trades positioned to profit from their forced exit. Machine learning classifiers confirm spoofing probability while an LSTM model tracks the manipulation lifecycle, ensuring you're trading with—not against—the inevitable unwind.
The system combines eight behavioural metrics (High Quoting Activity, Unbalanced Quoting, Abnormal Cancellations, Low Execution Probability, Trades Opposing Quotes, Cancels Opposing Trades, and Cyclical Patterns in Depth and Cancellations) into a unified spoofing probability score. Random Forest and Boosted Tree classifiers process these features, then merge via logistic regression to generate a real-time probability between 0 and 1. When this score exceeds 0.95, an LSTM-based RNN trained on court-validated spoofing cases classifies the current manipulation phase (stacking, reaction, or extraction), determining optimal entry timing and trade direction opposite the spoofed side.

DEX Array identifies the moment a trader places a high-contract size limit order within the bid/ask spread, indicating a clear signal of directional conviction. The scanner validates whether that intent translates into real price movement, filtering out noise through multi-layered confirmation: no spoofing contamination, high-probability directional forecasting, and aligned volatility signals. Trades execute when all conditions align, capturing momentum from informed aggression before it fully materialises.
Detection triggers when an order price satisfies P(bid) < P(order) < P(ask), indicating aggressive placement between the bid and ask. The scanner then verifies whether subsequent bid/ask updates absorb this order and drive directional movement. Three validation gates must align: Spoofing Score below 0.4, Microprobability Forecast exceeding 0.82 in the same direction, and the proprietary HF Omega metric returning positive. Only when all conditions hold does the system execute, ensuring each trade captures genuine aggressive intent rather than transient noise.

Trespass reads structural tension in the order book by aggregating bid-ask depth across multiple levels, isolating authentic liquidity shifts from transient noise and quote stuffing. High-frequency execution meets precision filtering: trades trigger only when imbalance strength, directional probability, and spoofing absence all align. Trespass delivers consistent, volume-driven alpha across changing market conditions, handling execution latency effectively while adapting to different market regimes.
The core mechanism employs tick stacking, aggregating volume across L levels (typically 3-5) on both sides to compute the imbalance ratio (total buying/selling pressure divided by total volume). This raw signal gives us the relative normalised imbalance ratio, which then passes through proprietary noise filtering to remove quote stuffing and transient pressure. Trades execute when the filtered imbalance breaches configurable thresholds (typically >0.5), Microprobability score exceeds 0.8 in the same direction, and Spoofing probability remains under 0.2. Exit logic is risk-profile dependent, closing positions early if market conditions deteriorate during the execution of a trade.

A machine learning-based forecasting model that predicts the next 2 ticks of price movement using top levels of order book data. Included Exclusively in Forecast.