Summary of "What is a Realistic Monthly % Return in Trading?"
Finance-focused summary (trading returns, risk, and drawdowns)
- The presenter argues that realistic monthly trading returns depend primarily on risk per trade, not on narratives like “best traders can only make X%.”
- He uses a backtested + live-traded simple moving average (SMA) crossover trend-following strategy over ~9 years with ~600–700 trades.
- He frames trading goals relative to the S&P 500:
- S&P 500 historical average: about 10%/year (over ~78 years per his description).
- In the referenced period, the S&P 500 averaged ~12.8% CAGR (per his stated comparison timeframe).
- He claims traders should aim to outperform the S&P 500 on a risk-adjusted basis; otherwise, it’s not worth trading versus passive investing.
Assets / instruments mentioned
- Gold (traded by his trend-following strategy/portfolio)
- NASDAQ (used as an equity proxy/instrument in his strategy description)
- Forex (separate strategy/portfolio mentioned)
- S&P 500 (benchmark index)
(No specific stock tickers, ETFs, bonds, or crypto were mentioned in the subtitles.)
Key strategy framework (as described)
- Trend-following SMA crossover approach.
- Performance evaluation uses:
- Average annual return across the test period
- Implied monthly return derived by dividing annual return by 12
- Drawdown metrics, where drawdown is defined as:
- Account down 5% or more from a peak
- Win rate
- Drawdown size and duration
- He emphasizes that changing only position/risk sizing (risk % per trade) can show the return vs. drawdown tradeoff.
Method: risk-based return expectations + drawdown outcomes
Baseline: risk ~1% per trade
- Strategy trades gold + NASDAQ (trend following).
- Performance:
- Average annual return: 31%
- Implied average monthly return: 31% / 12 ≈ 2.5%
- Drawdowns:
- Peak drawdown observed: about 12% (cites examples like 2018)
- Average drawdown size: about 6%
- Average drawdown duration: about 87 days
- Number of drawdowns: ~14 drawdowns over a 9-year period (per his stated count)
His conclusion: realistic “monthly return expectations” can be around ~2.5%/month, but only for the assumed risk level.
Higher risk: risk ~2% per trade
- Performance:
- Average annual return: 66%
- Implied average monthly return: 66% / 12 ≈ 5.5%
- Drawdowns:
- Max drawdown: 23% (vs. ~12% in baseline)
- Average drawdown: about 10%
He highlights that monthly return more than doubles, but drawdowns increase substantially.
Even higher risk: risk ~5% per trade
- Performance:
- Average annual return: 193%
- Implied average monthly return: 193% / 12 ≈ 16%
- Drawdowns:
- Max drawdown: 51% (described as “half of your money gone”)
- Number of drawdowns: ~57 drawdowns (accounts falling 5%+)
- Average drawdown: about 11%
He cautions this drawdown level may break psychological discipline for many investors.
Additional performance comparisons / examples
- He claims the strategy outperforms the benchmark:
- Strategy: 31% CAGR (under his baseline risk scenario)
- S&P 500: ~12.8% CAGR (in the same general comparison period)
- Sample yearly returns from the baseline strategy:
- 2017: 36%
- 2018: 5%
- 2019: 57%
- 2020: 38%
- He references other years as part of the variability pattern (numbers indicate large year-to-year swings).
How his own risk choices differ by strategy (forex vs. trend-following)
- He uses Trader Waves (a trading journal) to track performance.
- Trend-following portfolio (gold + NASDAQ):
- Uses risk assumptions aligned with its higher win-rate / drawdown tolerance (references 1% per trade in the theoretical strategy case).
- Forex strategy:
- Lower win rate (cites ~37% win rate for “this month so far”)
- More frequent trading:
- Trend-following: ~6 trades per month
- FX: ~10 trades per week (and mentions a month with 19 trades)
- Therefore risks ~0.5% per trade
- He claims this can still target around ~2% per month (in his example)
Public tracker (2025 forex, since March—per his description)
- Return examples:
- March 2025: +3%
- Another month shown: -1.96%
- September: down ~3%
- October: currently up +1.09%
- He claims that doubling risk (e.g., 0.5% → 1% per trade) would roughly scale monthly results (e.g., +5% → ~+10%, -3% → ~-6%), reinforcing the role of risk sizing.
Explicit recommendations / cautions
- He rejects a fixed rule like “best traders only make 2.5% per month.”
- Instead, set expectations based on:
- How much risk per trade you take
- How large drawdowns you can tolerate
- Strategy characteristics, especially:
- Win rate (he claims lower win rates typically produce larger drawdowns)
- Trade frequency
- He suggests many traders’ typical psychological tolerance is around ~10%–15% max drawdown.
- For larger accounts, drawdowns like 40%–50% are described as psychologically and financially harder to endure (example: $1M account, 50% drawdown = $500k lost).
Disclosures / disclaimers
- The subtitles do not include a formal “not financial advice” disclaimer.
- While there is an implicit caution via the drawdown tolerance discussion, no explicit regulatory disclaimer appears in the provided text.
Presenters / sources
- Presenter: Unnamed narrator/host (no name given in subtitles)
- Software referenced: Trader Waves (trading journal)
- Benchmark referenced: S&P 500 (index)
Category
Finance
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