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)