Sage Group RSI Strategy

No. RSI < 30 indicates the stock is oversold but doesn't guarantee a bounce. Wait for RSI to cross back above 30 (exit oversold) with a bullish candle confirmation. Also apply filters: trend context, volume, sector. Blindly buying at RSI < 30 leads to catching falling knives.

RSI 14 (14-period) is standard - smoother, fewer signals, more reliable. RSI 9 is faster - more signals but also more noise and false signals. For swing trading Sage, RSI 14 is recommended. RSI 9 might work for intraday but requires quicker reaction time.

Yes, especially in strong downtrends. During major selloffs, RSI can stay below 30 for days or weeks. This is why waiting for RSI to EXIT oversold (cross back above 30) is important - it confirms selling pressure is actually abating, not just pausing.

Sage is a steadier, lower-beta large-cap software company, so it reaches RSI extremes less frequently than higher-beta IT-services names like Computacenter or Kainos - but its signals tend to be cleaner and it has far better liquidity and listed options. Peers offer more frequent RSI extremes (more opportunities) but thinner options liquidity and larger, more erratic swings. Choose based on whether you prioritise signal frequency or tradability.

The daily timeframe is recommended for beginners - it's less noisy and requires checking only once per day at market close. Hourly can be used for entry timing once you have a daily signal. Avoid lower timeframes (15-min, 5-min) until you're experienced - they generate too many false RSI signals.

Divergence shows potential reversal but isn't an immediate signal. Wait for price confirmation: for bullish divergence (lower price low, higher RSI low), wait for price to break above a recent swing high. Combine with support/resistance levels. Divergence at support is more actionable than divergence in 'air'.

Use mean reversion (extremes) when ADX < 20-22 (ranging market). Use momentum (RSI > 55 or < 45 with trend) when ADX > 25 (trending market). Check trend context too - mean reversion works better when the RSI extreme is counter to the primary trend (oversold in an uptrend).

Weekly RSI sets the primary bias (above/below 50). Daily RSI provides signals. Hourly RSI times entries. Best trades: Weekly bullish + Daily oversold bounce = high-probability long. Avoid: Weekly bearish but buying a daily oversold - lower probability as the primary trend is against you.

For an oversold bounce: buy ATM calls or bull call spreads (reduced cost). For an overbought reversal: buy ATM puts or bear put spreads. Use shorter-dated options for quick mean reversion expected in 3-5 days, monthly options for longer setups. Spreads are cost-effective for moderate-conviction signals. ICE single-stock options cover 1,000 shares and expire the third Friday.

Very important. Volume confirms RSI signals. An oversold bounce with a volume spike = buyers arriving (good). An oversold bounce with no volume = weak signal. High-volume new lows despite RSI < 30 = more downside likely. Always check the volume pattern before acting on RSI signals.

Calculate the ATR percentile over 100 days. High ATR (top 20%): use 25/75 thresholds. Low ATR (bottom 20%): use 35/65. Alternatively, use RSI Bollinger Bands - oversold when RSI touches its own lower band regardless of absolute level. This adapts to the current volatility regime automatically.

A failure swing is an internal RSI pattern. Bullish: RSI < 30, bounces to 40-50, retests but stays > 30, breaks above the prior bounce high. This shows a momentum shift before price confirms. Trade when RSI breaks its prior bounce high, with a stop below the retest low. Higher probability than simple oversold readings.

Build conditional win-rate tables from your signal history: bucket past signals by ADX, days-oversold, volume ratio, sector RSI and days-to-results, then measure the win rate within each bucket. Trade only the buckets with a demonstrated, walk-forward-stable edge (typically ADX < 22 and days-oversold > 2), and size by the strength of that edge within strict risk caps. This is transparent, rule-based filtering - not automated AI screening - and it lifts the effective win rate by skipping the contexts that historically fail.

Use walk-forward analysis: train on 3 years, test on 1 year, roll forward and repeat. Test multiple parameters but don't over-optimise. Measure the Sharpe ratio and max drawdown, not just win rate. Require 100+ trades for statistical significance (on a lower-signal name like Sage, this may mean a longer history or pooling with peers). Compare to a buy-and-hold benchmark. Recalibrate every 1-2 years.

Calculate the strategy's historical volatility. Target an overall portfolio volatility contribution. If the strategy has 18% volatility and you target 12%, scale the allocation by 12/18 = 0.67. Use fractional Kelly for position sizing within the allocation. Limit correlation with other UK tech strategies - they move together, so treat them as a single strategy for diversification purposes.