Find the market opportunity. Build the strategy.
Lakefront is an agentic AI framework for market research and trading ideas. It combines technical analysis, market analysis, and deep strategy workflows to surface opportunities, explain what is driving them, and build the specific options structure that best fits the thesis.
Built for PMs, analysts, and options desks that want repeatable research before execution.
Agentic framework
Market opportunity discovery
technical analysis, market analysis, and AI research
Flagship deep dive
Intermarket multiple regression
find the relationships that actually drive the setup
Strategy design
Specific options structures
turn thesis into a defined-risk trade
Operating standard
Confirmation + risk discipline
institutional decision framework
Workflow state
The research engine has escalated a qualified setup and the strategy builder is packaging the cleanest expression.
Two-stage process
From market research to strategy-specific trade design.
The first workflow behaves like an analyst: it researches the setup, finds the relationships that matter, and identifies where a deep dive is justified. The second workflow behaves like a trader: it builds the specific structure around timing, volatility, payoff, and risk.
Intermarket research model
A small set of related markets is explaining the setup better than surface-level noise
Driver count
3 markets
Model fit
R² 0.59
Signal state
Watch
Strategy builder
Candidate structures ranked by thesis fit
Confirmation and context
Leading signals are framed with volatility, catalyst, and confirmation logic before trade design
Workflow 01
Agentic market research and signal discovery
Workflow 02
Intermarket regression deep dive and strategy design
Operator frame
Confirmation, payoff shape, and risk limits
Workflow 01
Flagship deep dive: intermarket multiple regression analysis.
Intermarket relationship map
3 drivers
Small, effective model
Related markets ranked by explanatory value
Independent drivers
3 markets
small, effective set
Explained relationship
R² 0.59
quantitatively validated
Confirmation state
Watch
leading signal, confirmation required
Preferred structure
Call diagonal
defined risk
Customer benefit
Cleaner research, clearer drivers, better strategy context
Less noise
Focus on related markets
More clarity
Rank what matters most
Better timing
Wait for confirmation
Specific trade design
Not just a signal
Institutional framing
Risk and catalyst aware
Mandate filter
Mandate review
The workflow explains the benefit of the analysis without exposing the secret sauce: it shows the customer which relationships matter, why the setup is interesting, and how the trade can be structured responsibly.
Checklist
- Start with economically related markets instead of mechanical co-movers.
- Keep the model small, independent, and explainable.
- Require confirmation before escalating the trade idea.
Workflow 02
Once the research is clear, the strategy workflow gets specific.
Agentic market research
Lakefront begins with agentic market research across technical structure, macro context, sector leadership, volatility, rates, credit, and cross-asset behavior to surface where opportunity is worth deeper work.
Intermarket multiple regression deep dive
One flagship deep dive is intermarket multiple regression analysis: the workflow isolates a small set of economically related markets, removes redundant noise, ranks what matters most, and turns those relationships into a leading research signal.
Strategy-specific trade design
Once the research is clear, the strategy engine builds the specific options expression around strike, tenor, payoff geometry, volatility regime, and risk so the output is actionable instead of theoretical.
What the customer gets
Research output that reads like an analyst note, not a black box.
Research thesis
The workflow starts with technical and market analysis, then escalates only the setups that deserve a deeper intermarket investigation.
Agentic discovery layer
Model discipline
The regression deep dive focuses on a small set of related, non-redundant markets, then uses quantitative ranking to show which drivers actually matter.
Textbook-informed methodology
Confirmation logic
The signal is treated as a leading indicator, not an automatic trade. Confirmation and context are required before the strategy builder is allowed to act.
False-positive control
Trade design
The final output is a clean research memo and a strategy-specific options structure with payoff, timing, and risk already framed for decision-making.
Execution-ready output
Agentic workflow details
Research, validation, strategy design, and risk framing stay in one operating surface.
Research inputs
Technical structure
Trend, range, momentum
Market context
Volatility and positioning
Cross-asset map
Rates, dollar, credit
Catalyst path
Event timing
Regression discipline
Related markets
Economically meaningful
Redundancy control
Avoid overlap
Driver ranking
Standardized impact
Model simplification
Keep what matters
Strategy engine
Strike selection
Payoff geometry
Tenor selection
Time horizon
Structure choice
Spread / diagonal / fly
Volatility fit
Surface-aware pricing
Operator controls
Max loss
Defined upfront
Confirmation
Reversal / context
Volatility risk
Surface check
Exit logic
Scenario-based