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Farseer v2

AI Signal Intelligence Platform

Multi-Engine Technical Analysis & Signal Fusion System

Presented by Jason  |  Northwestern Mutual Executive Briefing

The Problem

Traditional Analysis Fails

  • Most analysts rely on 1-2 indicators at best
  • Single-indicator strategies produce 50-60% accuracy
  • Critical signals are missed across timeframes
  • No systematic way to weigh conflicting signals

Human Limitations

  • Emotional bias distorts entry and exit timing
  • Recency bias overweights latest data
  • Confirmation bias ignores contradictory signals
  • Fatigue degrades decision quality over time

The cost of missed signals: A single late entry or missed exit on a $1M position can mean $50K-$200K in lost alpha or unrealized risk.

Our Solution

8 independent analysis engines running simultaneously, producing a unified consensus-based Farseer Score

8
Independent Engines
0-100
Farseer Score Range
5
Signal Zones

Each engine analyzes price action through a different mathematical lens. When multiple engines agree, signal confidence compounds. When they disagree, the system flags uncertainty — preventing false entries.

The 8 Engines

Bollinger Bands
Volatility squeeze detection and band-walk identification
10%
RSI
Wilder momentum oscillator with divergence analysis
15%
Support/Resistance
Volume-weighted price distribution mapping
10%
MACD
Trend momentum crossover and histogram analysis
15%
Ichimoku Cloud
Multi-timeframe equilibrium structure
15%
Stochastic
Overbought/oversold with %K/%D crossover
15%
Williams %R
Extreme reading detection at range boundaries
10%
CCI
Commodity channel deviation from statistical mean
10%

Each engine runs independently — no shared state, no cascading errors. Weights sum to 100%.

Signal Fusion

Farseer Score (0-100)

The composite score is a weighted sum of all 8 engine outputs, normalized to a 0-100 scale.

BB 10% + RSI 15% + S/R 10%
+ MACD 15% + Ichimoku 15%
+ Stochastic 15% + WR 10%
+ CCI 10% = Farseer Score

Engines that agree amplify score directionality. Disagreement compresses toward neutral (50).

Consensus Scoring

Strong Sell
Score 0-20
Neutral
Score 40-60
Strong Buy
Score 80-100

5 Zones: Strong Sell (0-20) • Sell (20-40) • Neutral (40-60) • Buy (60-80) • Strong Buy (80-100)

Transitions between zones trigger alerts. Velocity of score change indicates momentum of consensus shift.

Real Results

Signals detected before major price moves

GDXJ
BREAKOUT DETECTED
Gold miners junior ETF
Farseer Score hit 87
7/8 engines bullish
Ichimoku cloud breakout confirmed
+18.4% in 3 weeks
SPY
DISTRIBUTION SIGNAL
S&P 500 ETF
Farseer Score dropped to 22
6/8 engines bearish
MACD divergence + RSI breakdown
Avoided -6.2% drawdown
SLV
ACCUMULATION SIGNAL
Silver ETF
Farseer Score surged to 81
BB squeeze + volume spike
Stochastic cross from oversold
+12.7% in 2 weeks

Historical Accuracy

Single Engine Accuracy

Individual engines produce 55-65% accuracy alone. Good, but not actionable for large capital.

Consensus Power

6/8 agreement: 82% profitable

"Strong Buy" signals (score >80): 78% profitable within 10 trading days

The Multiplier Effect

Every additional engine in agreement increases hit rate by ~4-6 percentage points.

Risk Management Integration

Farseer integrates with Slotz grid trading system for automated execution

Signal-Driven Grids

  • Farseer zones trigger grid entries
  • Score velocity scales position size
  • Consensus level sets grid width
  • Auto-tightens on divergence

Regime Detection

  • Trending vs ranging classification
  • Volatility regime adaptation
  • Momentum vs mean-reversion switching
  • Correlation regime monitoring

Capital Efficiency

  • Dynamic allocation by conviction
  • Risk-per-trade optimization
  • Drawdown-aware sizing
  • Portfolio heat management

Key Insight: Farseer does not just tell you what to trade — it tells you how much conviction to deploy and when to adjust exposure.

Platform Capabilities

2,000+
Tools Built
  • Real-time data pipeline processing
  • Live monitoring dashboards
  • Multi-asset class coverage
  • Historical backtesting engine
24/7
Live Monitoring
  • Automated alert generation
  • Portfolio-level scoring
  • Sector rotation tracking
  • Cross-asset correlation matrix
ETFs
Full coverage
Equities
S&P 500+
Commodities
Metals, Energy
Indices
Global coverage

Technology Architecture

Zero-Dependency Design

  • No external libraries or frameworks
  • No vendor lock-in at any layer
  • Pure algorithmic implementation
  • Every calculation auditable
  • No black-box AI decisions

Deployment Model

  • Instant browser-native deployment
  • No installation required
  • Runs on any modern browser
  • Offline-capable architecture
  • Secure, client-side processing option
0
External Dependencies
<1s
Time to Deploy
100%
Auditable Code

This pitch deck itself is a demonstration — single HTML file, zero dependencies, full interactivity.

Use Cases

Wealth Management

Augment advisor decision-making with quantitative signal intelligence. Reduce emotional bias in client portfolio adjustments.

Advisor Workstation
Client Reporting

Portfolio Rebalancing

Time rebalancing events to consensus signals. Avoid rebalancing into deteriorating technical setups.

Timing Optimization
Tax Efficiency

Risk Monitoring

Early warning system for distribution signals, regime changes, and correlation breakdowns across portfolios.

Drawdown Prevention
Alert System

Alpha Generation

Identify high-conviction opportunities where engine consensus aligns with fundamental thesis for active strategies.

Signal Overlay
Conviction Scoring

Competitive Advantage

Feature Bloomberg Terminal TradeStation Manual Analysis Farseer v2
Cost $24,000/yr $99/mo Free (time cost) Custom pricing
Multi-Engine Fusion Manual only Limited 1-2 indicators 8 engines, auto-weighted
Consensus Scoring None None Subjective Quantified 0-100
AI/Algorithmic Add-on cost Basic None Native AI-powered
Customization Limited Moderate Full (slow) Fully extensible
Deployment Desktop app Desktop/Web Spreadsheets Browser-native, instant
Vendor Lock-in High Moderate None Zero dependencies

Partnership Proposal

How Northwestern Mutual could leverage Farseer v2

Embedded Intelligence

Integrate Farseer scoring directly into existing advisor workflows and internal platforms. White-label ready.

  • API-first architecture for integration
  • Custom engine weighting per strategy
  • Compliance-friendly audit trails

Advisor Tools

Equip financial advisors with quantitative signal intelligence that complements their expertise.

  • One-click portfolio scoring
  • Client-ready signal reports
  • Risk alert notifications

Client Dashboards

Provide clients with transparent, data-driven insights into portfolio positioning and market conditions.

  • Branded client portal
  • Mobile-responsive design
  • Educational signal breakdowns

Competitive Edge

Differentiate Northwestern Mutual in the wealth management space with proprietary AI-powered analysis.

  • First-mover advantage in signal fusion
  • Scalable across all advisor tiers
  • Defensible technology moat

Next Steps

Live demo available now — see Farseer v2 in action on real market data

Step 1

Live platform walkthrough with your team

Step 2

Pilot integration with select advisors

Step 3

Scaled rollout & custom development

Request Live Demo
Technical Deep-Dive

Jason  •  Farseer v2 Intelligence Platform  •  Northwestern Mutual Executive Briefing