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Lesson 25: Risk Control and Psychology Management

#management #mentalhealth #productivity

Lesson 25: Risk Control and Psychology Management

⏱ Duration: 1.5 hours
🎯 Learning Objectives: Master the core elements of long-term stable profitability - risk control and psychology management

Course Overview

Technology, strategy, monitoring are all important, but what truly determines long-term success is risk control and psychology management.

Statistical Data Shows:

  • 90% of traders fail not because their strategy is poor, but because:
    • Poor risk control (excessive leverage, no stop loss)
    • Loss of psychological control (fear, greed, revenge trading)

Core Philosophy of This Lesson:

Control risk, manage psychology, to survive and profit long-term.

Part 1: Advanced Risk Control

1.1 Multi-layered Risk Control System

Layer 1: Single Trade Risk Control 

Single trade risk = Maximum loss per trade

Recommended configuration:
- Conservative: 1% of total funds per trade
- Moderate: 2% of total funds per trade
- Aggressive: 3% of total funds per trade (not recommended for beginners)

Example (total funds $10,000):
Single trade risk 2% = $200

If stop loss set at -4%:
Position size = $200 / 0.04 = $5,000

Configuration:
"stake_amount": 5000,
"stoploss": -0.04
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Important: Regardless of profit amount, single trade risk should never exceed 5% of total funds.

Layer 2: Total Risk Exposure Control 

Total risk exposure = Sum of all position risks

Recommended configuration:
- Conservative: Total risk ≤ 5% of total funds
- Moderate: Total risk ≤ 10% of total funds
- Aggressive: Total risk ≤ 15% of total funds

Example (2% single trade risk, max 3 positions):
Total risk exposure = 2% × 3 = 6%

If all 3 positions trigger stop loss:
Maximum loss = $10,000 × 6% = $600

Configuration:
"max_open_trades": 3,
"stake_amount": 5000,
"stoploss": -0.04
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Layer 3: Daily Loss Limit 

Set maximum daily loss limit, stop trading when triggered

Recommended configuration:
- Maximum daily loss: 3-5% of total funds

Implementation method:
1. Use MaxDrawdown protection
2. Or custom script monitoring

Configuration:
"protections": [
  {
    "method": "MaxDrawdown",
    "lookback_period": 24,  # 24 hours
    "trade_limit": 0,
    "stop_duration": 1440,  # Stop for 24 hours
    "max_allowed_drawdown": 0.05  # 5%
  }
]
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Layer 4: Weekly/Monthly Loss Limits 

Cumulative loss control:
- Weekly loss limit: -10%
- Monthly loss limit: -15%

Actions triggered:
- Weekly loss > -10%: Pause trading for 3 days, deep analysis
- Monthly loss > -15%: Stop trading, comprehensive strategy evaluation

Manual monitoring and execution (requires discipline)
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1.2 Dynamic Stop Loss Strategies

Strategy 1: Fixed Percentage Stop Loss (Basic) 

{
  "stoploss": -0.03
}
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Advantages:

  • ✅ Simple and clear
  • ✅ Controllable risk

Disadvantages:

  • ⚠️ Doesn't consider market volatility
  • ⚠️ May be too tight or too loose

Strategy 2: Trailing Stop Loss (Recommended) 

{
  "stoploss": -0.03,
  "trailing_stop": true,
  "trailing_stop_positive": 0.01,
  "trailing_stop_positive_offset": 0.02,
  "trailing_only_offset_is_reached": true
}
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Working principle:

1. Open position price: $100
   Stop loss: $97 (-3%)

2. Price rises to $102 (profit +2%, reaches offset)
   Trailing stop activates
   New stop loss: $102 × (1 - 0.01) = $100.98 (+0.98%)

3. Price rises to $105
   Stop loss moves up: $105 × (1 - 0.01) = $103.95 (+3.95%)

4. Price falls to $103.95
   Trailing stop triggers, locks +3.95% profit
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Advantages:

  • ✅ Protects profits
  • ✅ Lets profits run
  • ✅ Auto-adjusts

Strategy 3: ATR-based Dynamic Stop Loss (Advanced) 

# Custom stop loss in strategy
from freqtrade.strategy import IStrategy
import talib.abstract as ta

class DynamicStopLossStrategy(IStrategy):

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Dynamic stop loss based on ATR
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        # Calculate ATR
        atr = last_candle['atr']
        current_price = current_rate

        # Set stop loss at 2x ATR
        atr_stop_distance = (atr * 2) / current_price

        # Minimum stop loss -2%, maximum -5%
        stop_loss = max(min(-atr_stop_distance, -0.02), -0.05)

        return stop_loss

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add ATR indicator
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        return dataframe
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Advantages:

  • ✅ Adapts to market volatility
  • ✅ Wider stop loss in high volatility
  • ✅ Tighter stop loss in low volatility

1.3 Advanced Position Management

Method 1: Fixed Position Size (Already Learned) 

Fixed amount per trade
"stake_amount": 1000
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Method 2: Fixed Percentage 

Fixed percentage per trade
"stake_amount": "unlimited",
"tradable_balance_ratio": 0.33  # 1/3 of available funds per trade
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Method 3: Kelly Formula (Advanced) 

# Kelly formula to calculate optimal position
def kelly_criterion(win_rate, avg_win, avg_loss):
    """
    Kelly % = W - [(1-W) / R]
    W = Win rate
    R = Average win/Average loss (profit/loss ratio)
    """
    if avg_loss == 0:
        return 0

    R = abs(avg_win / avg_loss)
    kelly = win_rate - ((1 - win_rate) / R)

    # Conservative: use Half Kelly
    return max(0, kelly * 0.5)

# Example calculation
win_rate = 0.55  # 55% win rate
avg_win = 3.5    # Average win $3.5
avg_loss = -2.0  # Average loss $2.0

kelly_pct = kelly_criterion(win_rate, avg_win, avg_loss)
print(f"Kelly position: {kelly_pct*100:.1f}%")

# Output: Kelly position: 16.4%
# Means each trade should invest 16.4% of total funds
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Warning: Kelly formula is theoretically optimal but in practice:

  • ⚠️ Requires accurate win rate and profit/loss ratio (difficult to predict)
  • ⚠️ High volatility, high psychological pressure
  • ✅ Recommend using Half Kelly or Quarter Kelly

Method 4: Tiered Position Management 

Adjust position based on signal strength:

Strong signal (multiple indicators confirm):
"stake_amount": 1500  # 150% standard position

Medium signal (standard):
"stake_amount": 1000  # 100% standard position

Weak signal (uncertain):
"stake_amount": 500   # 50% standard position

Implementation (in strategy):
def custom_stake_amount(self, pair: str, current_time: datetime,
                        current_rate: float, proposed_stake: float,
                        min_stake: float, max_stake: float, **kwargs) -> float:

    dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
    last_candle = dataframe.iloc[-1].squeeze()

    # Calculate signal strength
    signal_strength = 0

    if last_candle['rsi'] < 30:
        signal_strength += 1
    if last_candle['macd'] > last_candle['macdsignal']:
        signal_strength += 1
    if last_candle['adx'] > 25:
        signal_strength += 1

    # Adjust position based on signal strength
    if signal_strength >= 3:
        return proposed_stake * 1.5  # Strong signal
    elif signal_strength == 2:
        return proposed_stake * 1.0  # Standard
    else:
        return proposed_stake * 0.5  # Weak signal
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1.4 Protection Mechanism Configuration

Protection 1: StopLossGuard (Stop Loss Protection) 

{
  "protections": [
    {
      "method": "StopLossGuard",
      "lookback_period_candles": 60,
      "trade_limit": 3,
      "stop_duration_candles": 30,
      "required_profit": 0.0
    }
  ]
}
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Function:

  • 3 stop losses within 60 candles
  • Pause trading for 30 candles
  • Prevent consecutive stop losses

Protection 2: MaxDrawdown (Drawdown Protection) 

{
  "protections": [
    {
      "method": "MaxDrawdown",
      "lookback_period_candles": 200,
      "trade_limit": 0,
      "stop_duration_candles": 50,
      "max_allowed_drawdown": 0.15
    }
  ]
}
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Function:

  • Drawdown exceeds 15% in 200 candles
  • Pause trading for 50 candles
  • Prevent continued losses

Protection 3: LowProfitPairs (Low Profit Protection) 

{
  "protections": [
    {
      "method": "LowProfitPairs",
      "lookback_period_candles": 360,
      "trade_limit": 4,
      "stop_duration": 120,
      "required_profit": -0.05
    }
  ]
}
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Function:

  • 4 trades for a pair lose >5% in 360 candles
  • That pair pauses trading for 120 minutes
  • Prevent single pair continued losses

Protection 4: CooldownPeriod (Cooldown) 

{
  "protections": [
    {
      "method": "CooldownPeriod",
      "stop_duration_candles": 5
    }
  ]
}
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Function:

  • 5 candles cooldown after each position close
  • Prevent immediate re-entry
  • Avoid emotional trading

Comprehensive Protection Configuration (Recommended) 

{
  "protections": [
    {
      "method": "StopLossGuard",
      "lookback_period_candles": 60,
      "trade_limit": 3,
      "stop_duration_candles": 30
    },
    {
      "method": "MaxDrawdown",
      "lookback_period_candles": 200,
      "stop_duration_candles": 50,
      "max_allowed_drawdown": 0.15
    },
    {
      "method": "LowProfitPairs",
      "lookback_period_candles": 360,
      "trade_limit": 4,
      "stop_duration": 120,
      "required_profit": -0.05
    },
    {
      "method": "CooldownPeriod",
      "stop_duration_candles": 3
    }
  ]
}
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Part 2: Psychology Management

2.1 Four Major Enemies of Trading Psychology

Enemy 1: Fear (Fear)

Manifestations:

  • Afraid to open positions after consecutive losses
  • Close positions early when profitable
  • Want to stop trading when seeing drawdowns
  • Doubt strategy effectiveness

Case Example:

Trader A's story:
- First 3 trades all stopped, lost $150
- 4th trade, strategy signals buy
- But due to fear, manually skipped signal
- Result: That trade made $80 profit
- Regretted, became more anxious
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Coping Methods:

  1. Accept that losses are normal

    • Even with 60% win rate, 40% will lose
    • 3-5 consecutive losses are probabilistic

  2. Trust backtest data

    • Review backtest results
    • Confirm strategy's long-term effectiveness
    • Short-term fluctuations don't indicate failure

  3. Reduce position size

    • If fear affects decisions
    • Temporarily reduce position by 50%
    • Regain confidence then increase

  4. Automated execution

    • Fully automated, no manual intervention
    • Avoid emotional impact on decisions 

#### Enemy 2: Greed (Greed)

**Manifestations**:
- Want to increase position size after profits
- Unwilling to take profits, want more
- Use leverage to pursue high returns
- Ignore risks, only see returns

**Case Example**:
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Trader B's story:

  • First month profit +15%, very excited
  • Decided to increase position, stake_amount: 1000 → 3000
  • Second month encountered -20% drawdown
  • Because position large, actual loss $1800
  • Not only lost last month's profit, but also went negative 

**Coping Methods**:
1. **Set realistic goals**
   - Monthly return 5-10% is already very good
   - Don't pursue unrealistic high returns
   - Stability is better than explosive profits

2. **Strictly follow position management**
   - Don't arbitrarily increase positions due to profits
   - Increase gradually as planned (like Lesson 23)
   - Single trade risk doesn't exceed 2-3%

3. **Take profits timely**
   - Take profits when targets reached
   - Don't expect "perfect" tops
   - Secure profits

4. **Record successes and failures**
   - Record failures caused by greed
   - Review regularly as reminders
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Enemy 3: Revenge Trading (Revenge Trading)

Manifestations:

  • Want to immediately "win back" after losses
  • Increase position or trade frequently
  • Ignore strategy signals, trade blindly
  • Emotional out of control

Case Example:

Trader C's story:
- BTC/USDT stopped, lost $100
- Mentally unbalanced, immediately manually opened ETH/USDT
- No signal confirmation, purely wanted to "win back"
- Result: Another stop loss, lost $120
- Emotional breakdown, daily loss $220
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Coping Methods:

  1. Accept that losses are costs

    • Losses are part of trading
    • Not personal failure
    • Accept calmly

  2. Set cooldown period

    • Rest 1-2 hours after large losses
    • Leave trading interface
    • Do other things to divert attention

  3. Enable CooldownPeriod protection

    • Forced cooldown period
    • Prevent impulsive trading

  4. Set daily loss limits

    • Automatically stop when limit reached
    • Prevent扩大损失 

#### Enemy 4: Overconfidence (Overconfidence)

**Manifestations**:
- Think you're "invincible" after consecutive profits
- Ignore risk management
- Modify strategies randomly
- No longer cautious

**Case Example**:
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Trader D's story:

  • Consecutive 2 weeks profit +20%
  • Thought they "mastered the market"
  • Turned off stop loss protection, used leverage
  • Market reversal, single-day loss -30%
  • Two weeks of profits all lost 

**Coping Methods**:
1. **Stay humble**
   - Market is always unpredictable
   - Today's profit doesn't guarantee tomorrow's
   - Luck plays a big part

2. **Stick to risk management**
   - Risk rules unchanged regardless of profits
   - Don't relax vigilance due to profits

3. **Regular review**
   - Analyze whether profits are from strategy or luck
   - Is it sustainable
   - Pressure test yourself
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2.2 Establish Trading Discipline

Trading Discipline Rules 

I promise to strictly follow the following discipline:

1. Risk management
   □ Single trade risk ≤ 2% total funds
   □ Total risk exposure ≤ 10% total funds
   □ Always set stop loss
   □ Don't use leverage (initially)

2. Strategy execution
   □ Trade strictly according to strategy signals
   □ Don't manually intervene in automated trading
   □ Don't modify parameters based on emotions
   □ Don't chase highs and sell lows

3. Adjustment rules
   □ Verify in Dry-run before any adjustment
   □ Adjust only one variable at a time
   □ Observe at least 7 days after adjustment
   □ Make decisions based on data, not emotions

4. Stop loss discipline
   □ Execute stop loss immediately when triggered, no hesitation
   □ Don't regret "should have not stopped loss" afterwards
   □ Accept stop loss as cost

5. Psychology management
   □ Rest 1 day after consecutive losses
   □ Analyze calmly, don't revenge
   □ Don't be blindly confident after profits
   □ Maintain long-term perspective

6. Continuous learning
   □ Review trading records weekly
   ▤ Analyze success and failure reasons
   ▤ Continuously learn and improve
   □ But don't frequently adjust strategies

Signature: __________  Date: __________

Discipline violation penalties:
- 1st violation: Stop trading for 3 days, deep reflection
- 2nd violation: Stop trading for 7 days, re-evaluate
- 3rd violation: Stop trading for 30 days, start over from learning
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2.3 Coping with Consecutive Losses

Consecutive Loss Handling Process 

3 consecutive losses:
□ Deep breath, stay calm
□ Check for technical issues
□ Confirm strategy logic is normal
□ Continue observing, no adjustment needed

5 consecutive losses:
□ Pause buying (/stopbuy)
□ Detailed analysis of each losing trade
□ Check if market environment has changed
□ Confirm if protection mechanisms triggered
□ Decide whether to continue or adjust

7 consecutive losses:
□ Stop trading at least 1 day
□ Comprehensive strategy evaluation
□ Compare with backtest and Dry-run
□ Consider reducing positions or switching strategies
□ Seek advice from others (community, mentors)

10 consecutive losses:
□ Stop trading at least 3 days
□ Strategy may have major issues
□ Re-backtest recent data
□ Consider returning to Dry-run
□ Or stop using this strategy
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Psychological Recovery Techniques 

Technique 1: Leave trading interface
- Close all trading-related pages
- Go for a walk or exercise
- Do other interested activities
- Rest at least 2-3 hours

Technique 2: Review success stories
- Browse previous profitable trades
- Remind yourself that strategy works
- Rebuild confidence

Technique 3: Reduce positions
- If psychological pressure high
- Temporarily reduce positions by 50%
- Reduce P/L fluctuations
- Restore calm mindset

Technique 4: Write trading diary
- Record feelings in detail
- "I feel fearful because..."
- "How should I improve..."
- Writing helps clear thoughts

Technique 5: Communicate with others
- Find trusted people to talk to
- Join trader communities
- Share experiences and feelings
- Get support and suggestions
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2.4 Long-term Mindset Cultivation

Professional Trader's Psychological Traits 

1. Probabilistic thinking
   - Don't judge success by single trades
   - Focus on long-term statistical performance
   - Accept short-term randomness

2. Emotional stability
   - Maintain equanimity with profits and losses
   - Don't be anxious about daily fluctuations
   - Process-oriented, not result-oriented

3. Patience
   - Wait for suitable entry opportunities
   - Don't rush to "get rich quick"
   - Enjoy the power of compounding

4. Continuous learning
   - Maintain curiosity
   - Continuously improve systems
   - But don't chase new strategies

5. Self-discipline
   - Strictly execute plans
   - Don't be swayed by emotions
   - Knowledge and action aligned
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Daily Mindset Practice 

Morning (before trading):
1. Deep breath 10 times, meditate 5 minutes
2. Review trading discipline rules
3. Set today's expectations:
   - "Today may profit or may lose"
   - "I focus on execution, not results"
   - "I trust my strategy"

During trading:
1. Maintain emotional stability
2. Don't check P/L frequently
3. Timed checks, not constant monitoring
4. Rest immediately when emotional fluctuations detected

Evening (after trading):
1. Record today's trades and mindset
2. Analyze objectively, no self-blame
3. Be grateful for today's learning opportunities
4. Prepare to continue execution tomorrow
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Part 3: Risk Management Tools

3.1 Risk Monitoring Scripts

Create risk_monitor.py:

#!/usr/bin/env python3
"""
Real-time risk monitoring script
Monitor current risk exposure, alert when thresholds exceeded
"""

import sqlite3
import requests
from datetime import datetime

DB_PATH = 'tradesv3.sqlite'
TELEGRAM_TOKEN = 'YOUR_BOT_TOKEN'
TELEGRAM_CHAT_ID = 'YOUR_CHAT_ID'

# Configuration
TOTAL_CAPITAL = 10000  # Total capital
MAX_RISK_PER_TRADE = 0.02  # Single trade risk 2%
MAX_TOTAL_RISK = 0.10  # Total risk 10%
MAX_DAILY_LOSS = 0.05  # Daily loss limit 5%

def send_alert(message):
    """Send Telegram alert"""
    url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage"
    payload = {
        'chat_id': TELEGRAM_CHAT_ID,
        'text': f"🚨 *Risk Alert* 🚨\n\n{message}",
        'parse_mode': 'Markdown'
    }
    requests.post(url, data=payload)

def get_current_risk():
    """Calculate current risk exposure"""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()

    # Get all open positions
    cursor.execute("""
        SELECT
            pair,
            stake_amount,
            stop_loss_pct
        FROM trades
        WHERE is_open = 1
    """)

    open_trades = cursor.fetchall()
    conn.close()

    if not open_trades:
        return {
            'open_trades': 0,
            'total_exposure': 0,
            'total_risk': 0,
            'risk_ratio': 0
        }

    # Calculate risk
    total_exposure = sum(t[1] for t in open_trades)
    total_risk_amount = sum(t[1] * abs(t[2]) for t in open_trades if t[2])
    risk_ratio = total_risk_amount / TOTAL_CAPITAL

    return {
        'open_trades': len(open_trades),
        'total_exposure': total_exposure,
        'total_risk': total_risk_amount,
        'risk_ratio': risk_ratio,
        'trades': open_trades
    }

def get_daily_pnl():
    """Get today's P/L"""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()

    today = datetime.now().date()
    cursor.execute("""
        SELECT SUM(close_profit_abs)
        FROM trades
        WHERE DATE(close_date) = ?
    """, (today,))

    result = cursor.fetchone()
    conn.close()

    daily_pnl = result[0] if result[0] else 0
    daily_pnl_ratio = daily_pnl / TOTAL_CAPITAL

    return daily_pnl, daily_pnl_ratio

def check_risk():
    """Check risk and alert"""
    risk_info = get_current_risk()
    daily_pnl, daily_pnl_ratio = get_daily_pnl()

    print(f"=== Risk Monitoring Report {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ===")
    print(f"Open positions: {risk_info['open_trades']}")
    print(f"Total exposure: ${risk_info['total_exposure']:.2f}")
    print(f"Total risk: ${risk_info['total_risk']:.2f} ({risk_info['risk_ratio']*100:.2f}%)")
    print(f"Today's P/L: ${daily_pnl:.2f} ({daily_pnl_ratio*100:.2f}%)")

    # Check alerts
    alerts = []

    # 1. Check total risk exposure
    if risk_info['risk_ratio'] > MAX_TOTAL_RISK:
        alerts.append(
            f"⚠️ Total risk exposure too high: {risk_info['risk_ratio']*100:.2f}% "
            f"(Limit: {MAX_TOTAL_RISK*100:.0f}%)"
        )

    # 2. Check daily loss
    if daily_pnl_ratio < -MAX_DAILY_LOSS:
        alerts.append(
            f"⚠️ Today's loss exceeds limit: {daily_pnl_ratio*100:.2f}% "
            f"(Limit: -{MAX_DAILY_LOSS*100:.0f}%)\n"
            f"Recommend stopping trading immediately!"
        )

    # 3. Check single trade risk (if detailed data available)
    for trade in risk_info['trades']:
        if trade[2]:  # If stop loss data available
            trade_risk = abs(trade[2])
            if trade_risk > MAX_RISK_PER_TRADE:
                alerts.append(
                    f"⚠️ {trade[0]} single trade risk too high: {trade_risk*100:.2f}% "
                    f"(Limit: {MAX_RISK_PER_TRADE*100:.0f}%)"
                )

    # Send alerts
    if alerts:
        message = "\n\n".join(alerts)
        message += f"\n\nCurrent positions: {risk_info['open_trades']}"
        message += f"\nTotal risk: ${risk_info['total_risk']:.2f}"
        send_alert(message)
        print("\n❌ Risk alert sent!")
    else:
        print("\n✅ Risk within controllable range")

    print("=" * 50)

if __name__ == '__main__':
    check_risk()
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Set scheduled checks:

# Check hourly
0 * * * * cd /path/to/freqtrade && python3 risk_monitor.py
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3.2 Trading Journal Tool

Create trading_journal.py:

#!/usr/bin/env python3
"""
Trading journal tool
Record daily trading and psychological state
"""

import json
from datetime import datetime

JOURNAL_FILE = 'trading_journal.json'

def load_journal():
    """Load journal"""
    try:
        with open(JOURNAL_FILE, 'r', encoding='utf-8') as f:
            return json.load(f)
    except FileNotFoundError:
        return []

def save_journal(entries):
    """Save journal"""
    with open(JOURNAL_FILE, 'w', encoding='utf-8') as f:
        json.dump(entries, f, indent=2, ensure_ascii=False)

def add_entry():
    """Add journal entry"""
    print("\n=== Trading Journal ===\n")

    date = input("Date (leave blank for today): ").strip()
    if not date:
        date = datetime.now().strftime('%Y-%m-%d')

    print("\n1. Trading data:")
    trades = input("Today's trades: ")
    pnl = input("Today's P/L (USDT): ")
    win_rate = input("Win rate (%): ")

    print("\n2. Psychological state (1-10 score):")
    emotional_state = input("Emotional stability: ")
    confidence = input("Strategy confidence: ")
    stress = input("Stress level: ")

    print("\n3. Discipline compliance:")
    followed_plan = input("Strictly executed strategy? (yes/no): ")
    manual_intervention = input("Manual intervention? (yes/no): ")

    print("\n4. Reflections:")
    what_went_well = input("What went well today: ")
    what_to_improve = input("What needs improvement: ")
    lessons_learned = input("Lessons learned: ")

    print("\n5. Tomorrow's plan:")
    tomorrow_plan = input("Tomorrow's focus: ")

    entry = {
        'date': date,
        'trades': trades,
        'pnl': pnl,
        'win_rate': win_rate,
        'emotional_state': emotional_state,
        'confidence': confidence,
        'stress': stress,
        'followed_plan': followed_plan,
        'manual_intervention': manual_intervention,
        'what_went_well': what_went_well,
        'what_to_improve': what_to_improve,
        'lessons_learned': lessons_learned,
        'tomorrow_plan': tomorrow_plan
    }

    entries = load_journal()
    entries.append(entry)
    save_journal(entries)

    print("\n✅ Journal saved!\n")

def view_journal():
    """View journal"""
    entries = load_journal()

    if not entries:
        print("\nNo journal entries yet\n")
        return

    print("\n=== Trading Journal Records ===\n")
    for i, entry in enumerate(reversed(entries[-10:]), 1):
        print(f"{i}. {entry['date']}")
        print(f"   Trades: {entry['trades']}, P/L: {entry['pnl']} USDT")
        print(f"   Mood: {entry['emotional_state']}/10, Stress: {entry['stress']}/10")
        print(f"   Lessons: {entry['lessons_learned']}")
        print()

if __name__ == '__main__':
    while True:
        print("\n1. Add today's journal")
        print("2. View recent journal")
        print("3. Exit")

        choice = input("\nSelect: ").strip()

        if choice == '1':
            add_entry()
        elif choice == '2':
            view_journal()
        elif choice == '3':
            break
        else:
            print("Invalid selection")
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Usage:

python3 trading_journal.py
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📝 Practical Tasks

Task 1: Calculate Your Risk Parameters

Based on your total capital and risk tolerance, calculate:

Total capital: $__________

Single trade risk: __________% (recommended 2%)
Single trade maximum loss: $________ (total capital × single risk%)

Maximum positions: __________ (recommended 3-5)
Total risk exposure: __________% (single risk × positions)
Total maximum loss: $________ (total capital × total risk%)

Daily loss limit: __________% (recommended 5%)
Daily maximum loss: $________ (total capital × daily loss limit)

Weekly loss limit: __________% (recommended 10%)
Monthly loss limit: __________% (recommended 15%)
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Task 2: Configure Protection Mechanisms

In config.json add protection mechanisms:

  • StopLossGuard
  • MaxDrawdown
  • LowProfitPairs
  • CooldownPeriod

Test if trigger conditions are reasonable.

Task 3: Sign Trading Discipline Pledge

Print or copy trading discipline rules, sign and post prominently in trading area. Read aloud before trading every day.

Task 4: Start Writing Trading Journal

From today, spend 10 minutes every evening writing trading journal. Continue for at least 30 days.

Task 5: Psychological Stress Test

Imagine the following scenarios, honestly answer how you would react:

Scenario 1: 5 consecutive losing trades, lost $500
You would:
A. Analyze reasons calmly, continue executing strategy
B. Pause trading, rest 1 day
C. Increase position size, try to "win back"
D. Immediately modify strategy parameters

Scenario 2: Single day profit $1000, exceeding expectations
You would:
A. Maintain calm, continue trading as planned
B. Get excited and increase position size
C. Take profits, reward yourself
D. Worry about tomorrow giving back profits

Scenario 3: Position at +10% but starts falling to +5%
You would:
A. Let trailing stop loss auto close
B. Manually close to lock profits
C. Continue holding, expect rise again
D. Regret not closing at +10%

Ideal answers:
Scenario 1: A or B
Scenario 2: A or C
Scenario 3: A

If your answers differ, consider reasons and develop improvement plan.
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📌 Key Points

Iron Rules of Risk Control 

1. Always set stop loss
2. Single trade risk ≤ 2% total funds
3. Total risk exposure ≤ 10% total funds
4. Stop trading when daily loss limit reached
5. Don't use leverage (initially)
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Key Points of Psychology Management 

1. Accept that losses are costs
2. Focus on process, not results
3. Maintain emotional stability
4. Strictly follow discipline
5. Continuous learning and improvement
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Long-term Survival Rules 

1. Small start, gradually increase
2. Control risk first, profit second
3. Stay humble, respect the market
4. Don't pursue explosive profits, pursue stability
5. Surviving is more important than anything
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🎯 Next Lesson Preview

Lesson 26: Custom Strategy Development

In the next lesson, we will learn:

  • How to write your own trading strategies
  • Complete strategy development process
  • Common technical indicator implementation
  • Strategy testing and optimization

Having mastered risk control and psychology management, you have the foundation for long-term survival. Next, we will enter advanced topics, learning how to develop your own strategies.

🎓 Learning Suggestions:

  1. Risk first: Always prioritize risk control
  2. Emotional management: Regularly reflect on your psychological state
  3. Write trading journal: This is the best way to improve
  4. Long-term perspective: Don't judge by single days
  5. Stay humble: The market will teach you humility

Remember: In trading, control what you can control (risk, discipline, psychology), accept what you can't control (market, luck, results).

This is the most important lesson in quantitative trading. If you can only remember one lesson's content, remember this one.

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