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FixedRiskRewardLoss: The Risk-Counting Accountant

Nickname: The Risk Calculator
Profession: Quant world's "accountant" — calculates risk/reward ratio for every single trade
Timeframe: 5 minutes (short-term player)

1. What Is This Thing?​

Simply put, FixedRiskRewardLoss is:

  • An educational example strategy (not for making money, for learning)
  • Demonstrates how to calculate risk/reward ratios
  • Demonstrates how to implement breakeven stoploss

Like an actuary who calculates "how much can I lose, how much must I win" before buying anything: "For this trade, I can lose max 100, must make at least 350 to be worth it!" 🧮

2. Core Config: Basically "Penny-Pinching"​

Stoploss Rules​

Hard stoploss: Cut at 10% loss (safety net)
Custom stoploss: Risk/reward ratio 3.5:1 + breakeven mechanism

Translation: This strategy's focus is teaching you money management, trading logic is secondary.

3. Entry Conditions: No Conditions​

🎯 Always Buy​

Core Logic: None

In Plain English:

"I'm just an example strategy, entry logic is whatever, the point is stoploss techniques!"

Code Translation:

# Entry conditions
dataframe.loc[:, "buy"] = 1  # Always buy

Roast: This entry condition is so simple it's speechless, but it's an example strategy, the point is teaching you money management! 🤣

4. Stoploss Mechanism: This Is the Main Event​

4.1 Risk/Reward Ratio 3.5:1​

Core Logic:

  1. Calculate initial risk (ATR × 2)
  2. Take-profit target = Risk × 3.5
  3. Ensure every trade makes at least 3.5x the risk

In Plain English:

"For this trade I can lose max 100U, so I must make at least 350U to be worth it!"

Code Translation:

# Risk calculation
risk = entry_price - initial_stoploss
take_profit_target = entry_price + risk × 3.5

Classic Lines:

  • "This is the main character of this strategy, entry logic is just a sidekick!"
  • "Learn this and you can calculate whether every trade is worth it!"

4.2 Breakeven Mechanism​

Trigger Condition: Profit reaches 1x risk

In Plain English:

"Made 1x risk (e.g., risk 100, made 100), quickly move stoploss to breakeven, this trade at least can't lose!"

Code Translation:

# Breakeven logic
if current_profit >= 1x risk:
    stoploss = entry_price + fees  # Breakeven

4.3 Three-Stage Stoploss​

StageConditionStoploss LevelIn Plain English
InitialJust enteredATR × 2 stoploss"Set a stoploss first, max lose this much"
BreakevenMade 1x riskEntry price + fees"Made enough, secure the bag first"
Take ProfitMade 3.5x riskTake-profit target"Reached target, cash out"

5. Exit Logic: No Technical Exits​

5.1 Technical Exit: Doesn't Exist​

# Exit signals
dataframe.loc[:, "sell"] = 0  # No exit signals set

In Plain English:

"I exit via stoploss, why would I need exit signals?"

Roast: This strategy is truly "minimalist", even saved on exit signals 🤣

6. This Strategy's "Personality Traits"​

✅ Pros (Praise Section)​

  1. High Learning Value: Top choice for learning money management
  2. Risk/Reward Ratio: Shows how to calculate and implement fixed risk/reward
  3. Breakeven Mechanism: Shows how to lock in principal after profit
  4. ATR Adaptive: Adjusts stoploss based on volatility
  5. Clean Code: Around 100 lines, easy to understand

⚠️ Cons (Roast Section)​

  1. Can't Live Trade: No entry logic, will lose money in live trading
  2. No Trend Filter: Buys regardless of market direction
  3. No BTC Correlation: Doesn't know when Bitcoin crashes
  4. No Technical Exit: Exits entirely via stoploss
  5. Example Nature: It's not meant to make money originally

7. When to Use It?​

Use CaseRecommended ActionReason
Learning Money ManagementHighly RecommendedThis is designed for learning
Learning Risk/Reward RatioHighly RecommendedStandard example of risk/reward ratio
Learning Breakeven StoplossHighly RecommendedStandard example of breakeven mechanism
Live TradingNot RecommendedNo entry logic
Modified for Live TradingConsiderableAdd entry logic and protections

8. Summary: How Is This Strategy Really?​

One-Line Review​

"An accountant that can't make money but can teach you how to manage it"

Who Should Use It?​

  • ✅ People who want to learn money management
  • ✅ People who want to learn risk/reward ratio calculation
  • ✅ Quant beginners (simple code)
  • ✅ People who want to develop their own strategies

Who Should NOT Use It?​

  • ❌ People who want to make money directly in live trading
  • ❌ Those looking for mature strategies
  • ❌ People who don't want to modify code

My Recommendations​

  1. Learning First: Don't think about live trading directly, learn first
  2. Understand Principles: Get the risk/reward ratio calculation
  3. Modify and Improve: Add your own logic on this basis
  4. Live Test: Consider live trading after modification and improvement

9. What Can You Learn From This Strategy?​

9.1 Core Technology: Money Management​

This strategy is one of Freqtrade's official examples, specifically demonstrating money management features.

You Can Learn:

  • Risk/reward ratio calculation and implementation
  • Breakeven stoploss mechanism
  • ATR adaptive stoploss
  • Advanced usage of custom_stoploss() function

9.2 Performance in Different Markets (Plain English Version)​

Market TypePerformance RatingPlain English Explanation
📈 Slow Bull/Ranging Up⭐⭐⭐⭐☆Risk/reward management works in trends
🔄 Wide Ranging⭐⭐☆☆☆May trigger stoploss frequently
📉 One-Way Crash⭐☆☆☆☆No trend filter, easy to lose consecutively
⚡️ Extreme Sideways⭐⭐☆☆☆Too little volatility, ATR stoploss may be too wide

One-Line Summary: Good for learning, forget about live trading

10. Want to Run This? Check These Configs First​

10.1 Pair Configuration​

ConfigRecommended ValueRoast
Number of Pairs10-20 pairsExample strategy, don't use too many
Quote CurrencyUSDTDon't use BTC/ETH as quote
Max Open Trades1-3 ordersControl risk
Timeframe5mMandatory, can't change

10.2 Hardware Requirements (This Strategy Is Friendly!)​

This strategy has minimal computation, very low VPS requirements:

PairsMinimum RAMRecommended RAMExperience
10-20512MB1GBRuns easily

Warning: This is one of the few quant strategies that can run on a Raspberry Pi 😅

10.3 Backtest vs Live Trading​

custom_info behaves differently in live trading vs backtest:

In Backtest:

  • Can use entry time index directly
  • Data is pre-loaded

In Live Trading:

  • Need to use get_loc(method="ffill") to find nearest data
  • Data is updated in real-time

Recommended Process:

  1. Understand code logic first
  2. Backtest to see stoplosseffects
  3. Paper trade to observe live behavior
  4. Modify and improve before considering live trading

11. Easter Egg: The Author's "Little Tricks"​

Look carefully at the code, you'll find interesting things:

  1. Detailed Comments: Lots of comments in the code

    "Afraid you won't understand, I'll write as clearly as possible!"

  2. Entry Logic Set Directly to 1: dataframe.loc[:, "buy"] = 1

    "My focus is teaching you money management, entry logic is whatever!"

  3. Risk/Reward Ratio 3.5: This value is empirical

    "3.5 is a good ratio, you can change it to 3 or 4!"

12. Final Final Words​

One-Line Review​

"An accountant that can't make money but can teach you to manage it"

Who Should Use It?​

  • ✅ People who want to learn money management
  • ✅ Quant beginners
  • ✅ People who want to develop their own strategies

Who Should NOT Use It?​

  • ❌ People who want to make money directly in live trading
  • ❌ People who don't want to modify code

Manual Trading Recommendations​

Manual traders can learn money management approach:

  • Calculate risk/reward ratio before every trade
  • Adjust stoploss to breakeven after certain profit
  • Adjust stoploss distance based on volatility

13. ⚠️ Risk Reminder Again (MUST READ This Section)​

This Is an Example Strategy!​

FixedRiskRewardLoss is an educational example strategy, not a production strategy:

This strategy has no entry logic, direct live trading will lose money.

Simply put: It's a textbook, not a money printer!

Hidden Risks of Example Strategies​

In live trading, simple logic can lead to:

  • Consecutive Losses: No entry conditions, random entry has low win rate
  • Ranging Market Whipsaw: May trigger stoploss frequently
  • No Trend Protection: Will buy even in downtrends

My Recommendations (Real Talk)​

1. Treat this strategy as a textbook, don't live trade directly
2. Understand risk/reward ratio calculation principles
3. Add your own entry logic on this basis
4. Add trend filters, BTC correlation and other protection mechanisms
5. Consider live trading after improvement

Remember: This is a textbook, after learning you need to write your own strategy!

Final Reminder: No matter how good the strategy, the market won't say hello before teaching you a lesson. Light positions for testing, survival is most important! 🙏