Trend Classifier

It depends on your trading style. For swing trading (holding days to weeks), use the daily timeframe as primary. For day trading, use 4-hour or hourly. For position trading (weeks to months), use weekly. Always check higher timeframes for context - the trend on a higher timeframe takes precedence.

Yes, absolutely. The daily might show an uptrend while the hourly shows a pullback (temporary downtrend). This is normal and actually provides opportunities - you can buy the hourly dip within the daily uptrend. The key is understanding the hierarchy: higher timeframes have more weight.

For daily timeframe trends, checking once at end of day is sufficient - daily trends don't change minute by minute. Set up alerts for classification changes so you don't miss transitions. Intraday traders may check more frequently but trends still don't change every few minutes.

In sideways markets, you have several options: 1) Stay out and wait for a trend to develop, 2) Use range-trading strategies (buy support, sell resistance), 3) Use options strategies designed for non-trending markets like iron condors. Avoid trend-following strategies in sideways markets.

For trend-following strategies, strong trends are generally better - higher probability of continuation. However, strong trends can also be extended and due for pullbacks. Weak uptrends may offer better entry points but have lower conviction. The key is matching your strategy to the trend strength.

This is common and actually informative. Conflicting signals often indicate transition periods. Resolution approaches: 1) Weight indicators (MA alignment is foundational), 2) Require majority agreement, 3) Use the scoring system where conflicts result in moderate scores, 4) Wait for clearer signals. Conflicts suggest reducing position size.

Use multiple confirmations: 1) Require 2-3 bars of new classification before acting, 2) Check multiple indicators (not just one), 3) Verify on multiple timeframes, 4) Look for actual price structure change (lower low in uptrend, higher high in downtrend), 5) Use hysteresis - don't flip on minor changes.

Not necessarily - weak uptrend is still uptrend. Consider: 1) Tightening stops rather than exiting, 2) Taking partial profits, 3) Not adding to position. Exit when trend changes to Sideways or Downtrend, or when your stop is hit. Trend weakening is a warning, not an exit signal by itself.

Check both market trend and sector trend: 1) Best setups are when market AND sector trends align with your trade, 2) Be cautious when trading against sector trend even if stock looks good, 3) For portfolio, overweight sectors with strong trends, underweight weak, 4) Use relative strength to find sector leaders.

Scale position based on quality: High quality trend = 100% of intended position, Medium quality = 75%, Low quality = 50%, Choppy = 25% or skip. Also consider combined with trend strength: Strong trend + High quality = maximum conviction. Weak trend + Low quality = minimal or no position.

Backtest and compare: 1) Run strategy with classifier filter vs without, 2) Compare risk-adjusted returns (Sharpe), not just total return, 3) Measure false signal rate, 4) Check classification accuracy vs hindsight optimal, 5) Evaluate across different market regimes. A good classifier should improve returns and reduce drawdowns.

HMM advantages: Probabilistic output, learns from data, captures regime dynamics. Rule-based advantages: Transparent, controllable, no training needed. Use HMM when: you need probability not just classification, you have enough data, regime dynamics matter. Use rule-based when: transparency is critical, quick deployment needed, or as fallback.

Major events (national elections, global crises, China policy shocks) can invalidate normal classification: 1) Reduce reliance on classifier during extreme events, 2) Increase confirmation requirements, 3) Use shorter lookback for indicators to adapt faster, 4) Override with manual assessment if needed, 5) Accept that classification lag is higher during rapid changes.

Different instruments have different volatility characteristics: 1) ADX thresholds may need adjustment (single bank stocks such as DBS may show higher ADX than the broad STI), 2) Test thresholds on instrument's historical data, 3) Use relative measures (percentile of ADX) rather than absolute, 4) Sector indices may need different parameters than broad indices.

Recommended architecture: 1) Data layer with efficient time-series storage (InfluxDB, TimescaleDB), 2) Parallel processing for multi-instrument (each instrument independent), 3) Microservice for classification that other services call, 4) Caching layer for frequently accessed classifications, 5) Event-driven alerts on changes, 6) API for integration with trading systems, 7) Monitoring dashboard.