Selected work
CASE STUDY / AI Systems

TradingView AI Market Analysis Automation

An OSYSTIC quantitative research workflow automating TradingView market analysis, temporal validation, and structural zone research. The project established a rigorous engineering boundary for market data integrity and research governance without claiming live trading readiness.

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“OSYSTIC delivered a structured and disciplined market-analysis automation workflow with strong attention to data integrity, repeatable research, and validation. The team communicated clearly, handled the technical requirements professionally, and produced a solid research-focused solution.”
Confidential Client
Client feedback
Concept illustration
01 / CONTEXT

The challenge

The engagement required preventing temporal leakage in time-series splits, enforcing strict OHLCV market-data integrity, and separating transient notebook states from robust research logic. Furthermore, exploratory trading models needed an explicit boundary against unsupported production-trading claims.

02 / ENGINEERING

What OSYSTIC built

OSYSTIC delivered a governed research architecture featuring deterministic input validation, reproducible feature transformations, chronological splitting, and post-closure provenance review. The framework separated raw statistical movement from constrained execution-oriented evaluation.

Engineering Highlights

  • Representative-universe correction: the workflow moved from a narrow symbol set toward day-by-day momentum-universe reconstruction.
  • Point-in-time discipline: event price, ATR context, VWAP, and time-normalized relative volume were reviewed against what was knowable at the interaction timestamp.
  • Structure-aware zones: Premarket High remained a primary liquidity level while later work tested a deterministic exhaustion/retracement zone with explicit anchoring/activation rules.
  • Path-dependent labels: outcomes were evaluated by threshold order and paired with MFE/MAE and time-to-event measures rather than relying only on static direction labels.
  • Execution boundary: raw excursion/EV findings were separated from constrained TP/SL-style approximation and outlier sensitivity.
  • Regime awareness: large behavioral differences across liquidity/momentum groups motivated conditional analysis rather than one global average.

Capabilities

  • tradingview
  • pine-script
  • quantitative-research
  • market-analysis
  • time-series
  • machine-learning
  • model-validation
  • data-governance

Public Evidence Boundary

Client identity is private. This public showcase contains sanitized documentation and architecture diagrams with independent Git history, excluding all confidential source code and datasets.

03 / DELIVERY

Outcomes and validation

  • Completed the initial indicator stage including signal-state and wick-aware interaction refinements.
  • Delivered a structured research framework encompassing dynamic universe construction, point-in-time features, and path-dependent labeling.
  • Successfully closed the engagement at a research-validation milestone, establishing transparent documentation of methodological limitations rather than deploying unverified production models.
CLIENT PERSPECTIVEPROJECT-LINKED CLIENT FEEDBACK
“OSYSTIC delivered a structured and disciplined market-analysis automation workflow with strong attention to data integrity, repeatable research, and validation. The team communicated clearly, handled the technical requirements professionally, and produced a solid research-focused solution.”
Confidential ClientQuant Research / TradingView
TradingView AI Market Analysis Automation
Project-linked client feedback

Related engineering expertise

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