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Asset Tools

The Assets tab is where InvestLens collects every stock and ETF that appears anywhere in your account’s portfolios. From here you can open a single name, and run analysis asset-by-asset: performance, technical levels, and a quick snapshot view.

assets_tab

1. Search and coverage

InvestLens includes a fast, search-first experience for finding tickers supported by our data vendor.

  • Search by ticker or name
  • Add the asset to a new portfolio by one click
  • Coverage depends on what is available from the upstream vendor

asset search

2. Asset Snapshot

The Snapshot drawer is a quick-look panel designed for fast context without leaving your workflow.

It typically includes:

  • Analyst rating distribution (Buy / Hold / Sell)
  • Beta indicator
  • 52-week price range with markers (Price, Target if available, 50D/200D moving averages)
  • Media Sentiment Index (when available)
  • Basic valuation and earnings-surprise context (when available)

asset snapshot

Snapshot is meant for triage and orientation. It is not meant to replace deeper research.

3. Technical levels and trading signal

InvestLens can compute simple, interpretable technical levels to help frame entry/exit zones.
This includes:

  • Support and resistance candidates based on recent swing lows/highs
  • Fibonacci retracement levels (commonly 38.2%, 50%, 61.8%)
  • Moving averages (short-term EMA and long-term SMA) using your app settings

3.1 What the Fibonacci signal does

The Fibonacci tool identifies a recent swing range (a recent high-to-low or low-to-high move), then derives retracement levels within that range.

  • In an uptrend, it highlights potential pullback levels (possible buy zones) and uses the recent high as a reference sell/exit level.
  • In a downtrend, it highlights potential bounce levels (possible sell zones) and uses the recent low as a reference buy/cover level.

InvestLens presents:

  • Candidate buy price
  • Candidate sell price
  • The retracement levels used to generate those candidates

This is a heuristic signal for planning, not a guarantee that price will behave this way.

fibonacci levels

4. Asset Performance Report

The Asset Performance Report mirrors the Portfolio Performance Report , but for a single asset.

asset performance report

5. Price Prediction

InvestLens includes a Price Prediction module; its second iteration is currently under development.

The first iteration of this module used an LSTM (Long Short-Term Memory) model, introduced in Hochreiter & Schmidhuber (1997), Long Short-Term Memory: https://www.bioinf.jku.at/publications/older/2604.pdf

5.1 Background

Classic recurrent neural networks struggled to learn long-range patterns because gradients tend to vanish or explode during training. LSTM addressed this with a gated “memory cell” design (input/forget/output gates) that helps preserve useful information across time steps.

5.2 Why LSTM can be a better fit than Transformers for short-term time-series prediction

Transformers are powerful, but for short-horizon signals (often noisy, low SNR, and relatively short lookback windows), LSTMs can be a more practical first choice:

  • Stronger inductive bias for sequential dynamics: Recurrence naturally models “what just happened matters most,” which often matches short-horizon behavior.
  • More sample-efficient in many settings: LSTMs can perform well with less data and fewer parameters, reducing overfitting risk when datasets are limited or regimes shift.
  • Lower compute and latency: LSTMs are typically lighter to train and cheaper to run for small-to-medium sequence lengths, which matters for iterative research workflows.
  • Less architecture overhead: Many time-series Transformers require careful design choices (positional encoding, attention variants, patching, normalization tricks) to behave well on non-text data.

Transformers shine when they have access to a large dataset with long-range dependencies.

When released, Price Prediction is intended to support research workflows by:

  • Producing forecast-based signals (directional cues, not trade instructions).
  • Generating simple scenario paths (for example: optimistic, neutral, and pessimistic cases).
  • Highlighting potential overbought or oversold conditions as a screening aid.

This feature will be rolled out once it meets internal reliability standards.

6. Notes and limitations

  • Technical levels are descriptive tools, not promises.
  • Results depend on the selected lookback window and available data history.
  • Snapshot fields may be missing depending on the asset and vendor coverage.