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How to Combine Fundamentals and Technicals: Building a Hybrid Screening Model with FMP APIs

Market participants often view fundamental and technical analysis as opposing disciplines, but the most durable alpha often resides at their intersection. Fundamental analysts risk catching falling knives by ignoring price action, while technicians often trade noise by disregarding intrinsic value. A hybrid screening model bridges this gap by filtering the investable universe for companies that meet specific financial thresholds and align with current market structure.

This approach reduces false positives. A value stock with deteriorating momentum is often a value trap, while a growth stock with a broken technical structure signals a distribution phase. By layering price indicators over ratio analysis, you create a two-step validation process that improves trade timing and conviction. To truly understand how to layer these datasets effectively, it is helpful to review how to build a Market Insight Framework that turns raw price and fundamental data into actionable signals.

The Rationale for Convergence

Pure fundamental screens often return stocks that are optically cheap but lack a catalyst. Without a technical reversal, capital remains dead for quarters. Conversely, technical screens often flag breakouts that lack the earnings support to sustain a long-term move. A hybrid model forces convergence by requiring a financial metric to pass a hurdle rate while simultaneously requiring a technical signal to confirm the entry. This aligns the "what to buy" with the "when to buy."

Screen 1: Value Confirmed by Trend (The JPM Example)

This screen targets companies trading below historical valuation multiples but showing signs of accumulation. The goal is to isolate a discount in Enterprise Value (EV) metrics paired with constructive Relative Strength Index (RSI) behavior.

Analyzing the Fundamental Baseline

We isolate valuation using EV to EBITDA from the Key Metrics API. A lower multiple relative to peers or historical averages suggests undervaluation, but context is critical.

  • For JPMorgan Chase & Co. (JPM), data sourced from the Key Metrics API shows an EV/EBITDA of roughly 11.69 for the 2024 fiscal year.
  • This represents a notable shift from the 2023 level of 7.65, suggesting the market is pricing in higher growth expectations or a premium for safety.
  • To validate if this premium is earned, we check operating efficiency using the Metrics Ratios API. The Net Profit Margin for 2024 stands at approximately 21.6 percent.
  • This margin expansion confirms that the higher multiple is supported by underlying profitability rather than speculative hype.

Identifying the Technical Entry

Value requires a trigger. We use the Relative Strength Index (RSI) to identify when the price stabilizes.

  • Tracking JPM on a 1-minute timeframe using the RSI API, the indicator fluctuated between 43 and 76 on December 12, 2025.
  • Price action sourced from the Full Chart API shows the stock closing at 318.52.
  • The RSI moving out of oversold territory or holding the median (50) during consolidation signals that buyers are stepping in to defend the valuation floor.

Screen 2: Quality Entered on Pullbacks (The KO Example)

High-quality companies rarely trade at deep value discounts. The strategy here is to identify superior profitability and enter during temporary technical weakness, a setup often missed by pure value investors.

Defining the Quality Hurdle

We define quality through Return on Equity (ROE) and margins, sourced programmatically via the Metrics Ratios API.

  • Coca-Cola (KO) demonstrates an exceptional ROE of roughly 42.8 percent for fiscal year 2024.
  • The Gross Profit Margin remains robust at approximately 61 percent.
  • These metrics indicate a durable competitive moat, sourced from the Key Metrics API, which justifies paying a premium over book value.

Timing the Entry with Technicals

We wait for a mean reversion. A "pullback" is defined as a short-term price decline within a long-term uptrend.

  • On December 12, 2025, KO shares traded near 70.52.
  • The RSI was hovering around 47-50 as per the RSI API.
  • This neutral RSI reading suggests the stock is digesting recent moves, offering a potential entry point before the next leg up.
  • Those interested in automating this specific signal should explore Building an RSI Buy/Sell Signal with FMP Data, which details the quantitative logic behind these triggers.

Screen 3: Dividend Safety Supported by Momentum (The PEP Example)

Yield strategies often fail when the underlying equity capital depreciates faster than the dividend pays out. This screen filters for safe yields that are supported by positive price momentum.

Verifying the Payout Safety

We look for Dividend Yield and Payout Safety to ensure the income stream is not at risk of a cut.

  • PepsiCo (PEP) shows a 2024 dividend yield of roughly 3.46 percent, sourced from the Key Metrics API.
  • Crucially, the Dividend Payout Ratio is approximately 0.75.
  • This indicates the payment is covered by earnings, though it is nearing the upper bound of conservative limits, warranting close monitoring of free cash flow.

Confirming Trend Direction

To avoid "yield traps," price must be above key moving averages. We utilize the Exponential Moving Average (EMA) sourced from the EMA API.

  • On December 12, 2025, PEP closed at 150.65.
  • This closing price is trading slightly above its 10-period EMA of 150.48.
  • Price holding above a short-term EMA indicates immediate bullish sentiment, reducing the risk of buying a stock in structural decline.

The Execution Grid

To implement this efficiently, create a binary scoring grid. A stock must pass both the fundamental and technical columns to warrant a capital allocation.

Ticker

Fundamental Screen

Metric Value (Source: Key Metrics/Ratios API)

Technical Screen

Signal Value (Source: Technicals API)

Action

JPM

EV/EBITDA < 12

11.69

RSI > 50

~53

Watch

KO

ROE > 30%

42.77%

Pullback (RSI < 50)

~48

Buy Dip

PEP

Yield > 3%

3.46%

Price > 10 EMA

150.65 > 150.48

Hold

The Edge Lies in Correlation

A hybrid screening model removes the blind spots inherent in single-discipline analysis. By combining valuation ratios from income statements with price action indicators like RSI and EMA, you align capital deployment with market timing. The data from FMP endpoints ranging from Key Metrics to Technicals provides the raw material, but the alpha comes from the correlation logic you apply. Start with these three templates and adjust the thresholds to fit your specific risk tolerance, ensuring that every trade is backed by both intrinsic value and market confirmation.

Frequently Asked Questions

What is the best technical indicator to pair with fundamental analysis?

The Relative Strength Index (RSI) and Moving Averages (MA) are the most effective. RSI helps identify entry points for fundamentally strong stocks, while MAs confirm the prevailing trend direction.

How often should I run a hybrid stock screen?

For most active strategies, running the screen weekly is sufficient. This captures significant price shifts without reacting to intraday noise, while fundamental data typically updates quarterly.

Can I use these screens for day trading?

These screens are designed for swing trading and medium-term investing. Fundamental metrics like ROE and EV/EBITDA do not change fast enough to be relevant for intraday scalping strategies.

Why is EV/EBITDA preferred over the P/E ratio?

EV/EBITDA accounts for debt and cash levels, providing a clearer picture of a company's total valuation, which is essential when comparing companies with different capital structures.

What FMP endpoints are required for this model?

You primarily need the Key Metrics API for valuation data, the Financial Ratios API for profitability metrics, and the Technical Indicator API for RSI and EMA values.

How do I detect a value trap using this model?

A value trap appears when a stock passes the fundamental valuation screen (appears cheap) but fails the technical screen (price is in a downtrend below key moving averages).

Does this strategy work for small-cap stocks?

Yes, but you should add a liquidity filter. Small-cap stocks often display volatile technical signals due to lower volume, so stricter technical confirmation is advised.