Fragmented tooling means applying the right tool in the wrong context. A six-stage pipeline — diagnosis, strategy selection, screening, ranking, trade plan, synthesis — fixes the sequencing problem.

How the Six7 Alpha Signal Workflow Replaces Three Separate Tools

Most intermediate traders have built up a fragmented research stack over time. A screener here, a charting platform there, maybe a Discord with trade ideas, a VIX dashboard someone shared on Twitter. The result is a workflow that looks professional on the surface but has a serious structural flaw: every tool operates in isolation, and stitching them together requires you to hold the context in your head.

That context-switching tax is expensive. You check the VIX, pivot to your screener, lose your train of thought about sector rotation, and end up taking a trade that fits the screener criteria but not the market environment. Inconsistency compounds. Over time, your edge — if you have one — gets buried under execution noise.

The Six7 Alpha signal pipeline is built around a single insight: the steps of good trade research are always the same, and they should always happen in the same order.

The Problem With Fragmented Tooling

Before getting into the pipeline itself, it helps to name the specific failure mode fragmented tools create. The issue is not that individual tools are bad — Finviz is excellent at screening, TradingView is excellent at charting. The issue is that using them independently means you apply them without regard for sequence.

A trader who screens for RSI pullbacks on a day when the market is in a volatility spike and sector rotation is chaotic will find plenty of candidates. They will also be taking setups that have a much lower probability of working in that environment. The tool gave them results. The results were contextually wrong. The tool had no way to know that.

Good trading research is a pipeline, not a menu. Each step should inform the next.

Stage 1: Market Diagnosis

The workflow begins by establishing the current market regime. This means answering a few specific questions: Is the broad market trending or chopping? Is volatility elevated, compressed, or transitioning? Which sectors are receiving capital flows and which are being rotated out of?

The data inputs here include the McClellan Oscillator (MCO) for breadth, VIX levels and term structure for volatility regime, and sector-level relative strength for rotation context. This produces a classification: trending calm, trending volatile, high fear, low-volatility chop, or a mixed/transitional state.

This classification is the input to everything that follows. If you skip it, every downstream step is operating without its most important constraint.

Stage 2: Strategy Selection

Given the regime from Stage 1, the workflow filters to the strategies that are structurally suited to current conditions. The signal library covers 44+ distinct strategies across multiple categories: momentum, mean reversion, volatility-based, pattern-based, and sector-specific.

Not all strategies are appropriate at all times. An RSI pullback strategy — buying a brief dip in a stock that is in a strong uptrend — performs well in calm trending environments where the broader market provides a tailwind. It performs poorly in high-volatility regimes where dips tend to extend into reversals. A VIX pivot strategy, conversely, is specifically designed for fear spikes and is irrelevant in low-volatility conditions. Inside day breakout setups work best when volatility is compressed and about to expand.

Strategy selection is where most traders without a structured process make their biggest mistakes. They have a favorite setup and they look for it regardless of whether conditions support it.

Stage 3: Stock Screening

With a strategy selected, the screening step applies real quantitative filters to find candidates. This is not an AI generating a list of stocks that seem relevant — it is actual Finviz and yfinance data queried against strategy-specific criteria.

For an RSI pullback strategy in a calm uptrend, this means filtering for stocks above their 50-day moving average with RSI between 40 and 55 that have pulled back on below-average volume. The criteria are specific and mechanical. The screening produces a list of stocks that genuinely meet the setup requirements, not a curated list that sounds plausible.

This distinction matters. AI-generated stock lists without real data verification are essentially confident hallucinations. The screening here is verifiable and repeatable.

Stage 4: Ticker Ranking

Screening produces a candidate pool. Ranking selects from it. Not all candidates are equal — a stock that meets the setup criteria and also has strong relative strength against its sector peers, improving volume trend, and clean chart structure is a better candidate than one that barely clears the filters.

The ranking scores candidates across multiple dimensions and surfaces the highest-quality setups. This step handles the prioritization problem that plagues traders who manually screen: even a good screener returns 20-30 results, and deciding which three to actually trade requires a second layer of analysis.

Stage 5: Trade Plan

For the top-ranked candidates, the workflow generates a specific trade plan: entry price or condition, stop loss level, price target, and position size calibrated to current volatility. The stop is not arbitrary — it is placed at a technically meaningful level with position sizing adjusted so that a stop-out produces a defined loss as a percentage of account.

This step converts a setup idea into an actionable instruction. The difference between "I like this stock" and "I will buy at $142.50, stop at $138.00, target $152.00, sizing for a 1% account risk" is the difference between a thought and a trade.

Stage 6: Synthesis

The final stage is an LLM-generated synthesis that explains the reasoning across all previous stages, flags any risks or disqualifying conditions it detects, and provides market context. This is where language model capability is actually useful — not generating stock picks, but explaining and stress-testing a structured analysis that was built on real data.

The synthesis asks questions like: does this trade make sense given the current earnings calendar? Is there a sector-level headwind that the screening criteria would not have caught? Is the setup clean or are there conflicting signals worth noting?

What This Replaces in Practice

A trader doing this manually — checking breadth data, selecting a strategy, running a screener, ranking results, building a trade plan — is looking at 45 minutes to an hour per session, assuming they already know what they are doing. The pipeline compresses that to a few minutes and eliminates the most common failure mode: applying the right tool in the wrong context.

The value is not just speed. It is consistency. The same regime-to-strategy-to-screen-to-plan sequence runs every session. That consistency is what makes performance measurable and improvable over time.