Exit Strategies

Exit Strategies

Entries are easy, exits are everything. A mediocre entry with a disciplined exit will outperform a perfect entry with no exit plan. This skill covers systematic, rule-based exit methods for crypto and Solana token trading.

Why Exits Matter

• Entries determine if you participate. Exits determine how much you keep.
• Most traders spend 90% of effort on entries and 10% on exits — invert this.
• Without defined exits you rely on emotion, which guarantees inconsistency.
• Every trade should have three exits defined before entry: stop loss, take profit,

and trailing stop.

Exit Categories

1. Stop Loss — Risk Management Exits

Predefined price level where you close the position to cap downside.

| Method | Description | Best For | |--------|-------------|----------| | Fixed percentage | Exit at entry − X% | Simple setups, beginners | | ATR-based | Entry − ATR(14) × multiplier | Volatility-adaptive | | Support level | Below nearest swing low | Technically defined risk | | Maximum loss | Absolute SOL/USD cap | Account protection |

ATR-based stop (recommended default):

import pandas_ta as ta

atr = df.ta.atr(length=14)
stop_loss = entry_price - (atr.iloc[-1] * 2.0)  # 2x ATR below entry

Multiplier guide:

• 1.5× — Tight. High win rate needed. Good for scalps.
• 2.0× — Standard. Balances noise filtering with risk.
• 3.0× — Wide. For swing trades in volatile conditions.

See references/stop_loss_methods.md for complete methodology.

2. Take Profit — Target Exits

Predefined levels where you lock in gains.

Fixed risk/reward targets:

risk = entry_price - stop_loss_price
tp_2r = entry_price + (risk * 2)  # 2:1 R:R
tp_3r = entry_price + (risk * 3)  # 3:1 R:R
tp_5r = entry_price + (risk * 5)  # 5:1 R:R

Scaled exit framework (recommended for meme/PumpFun tokens):

| Tranche | Size | Target | Action After | |---------|------|--------|--------------| | 1 | 25% | 2× risk | Move stop to breakeven | | 2 | 25% | 3–5× risk | Trail remainder | | 3 | 25% | 5–10× risk | Tighten trail | | 4 | 25% | Trailing stop | Moonbag — let it ride |

Market cap milestone exits:

For PumpFun and meme tokens where R:R ratios are less meaningful:

milestones = [
    {"mcap": 50_000,  "sell_pct": 0.25, "label": "Cover cost"},
    {"mcap": 100_000, "sell_pct": 0.25, "label": "Lock profit"},
    {"mcap": 500_000, "sell_pct": 0.25, "label": "Major profit"},
    # Hold 25% as moonbag with trailing stop
]

See references/take_profit_strategies.md for full methodology including Fibonacci extension targets and volume-based exits.

3. Trailing Stop — Trend-Following Exits

Dynamic stops that follow price upward but never move down.

Percentage trailing:

def percentage_trailing_stop(
    current_price: float,
    highest_since_entry: float,
    trail_pct: float = 0.10,
) -> tuple[float, bool]:
    """Return (stop_level, triggered)."""
    highest = max(highest_since_entry, current_price)
    stop = highest * (1 - trail_pct)
    return stop, current_price  float:
    """Highest high over lookback minus ATR * multiplier."""
    highest_high = max(highs[-lookback:])
    return highest_high - (atr_value * multiplier)

EMA trailing:

# Exit when close  max_hold_bars and current_pnl  70:
    exit_reason = "rsi_overbought"

# MACD crossover exit
macd = df.ta.macd()
if macd["MACDs_12_26_9"].iloc[-1]  0.90:
    # Near graduation — decide: hold through or exit before
    # Graduation creates volatility spike, both up and down
    pass

if bonding_fill_pct  300:  # 5 min
    exit_reason = "stalled_bonding_curve"

Volume Decay Exits

buy_vol_1m = get_buy_volume(token, "1m")
buy_vol_5m = get_buy_volume(token, "5m") / 5  # Normalize to per-minute

if buy_vol_1m  ATR trailing > Take profit > Time stop.

The hard stop is always active and never overridden. The ATR trailing stop activates
after the first take-profit tranche fills. The time stop only fires if the trade is
not yet profitable.

## Common Exit Mistakes

| Mistake | Problem | Fix |
|---------|---------|-----|
| No stop loss | Unlimited downside | Always define max loss before entry |
| Moving stops wider | Increases risk after the fact | Never move stops away from price |
| Not taking profits | Winners become losers | Use scaled exits |
| All-or-nothing exits | Leaves money on the table or exits too early | Scale out in tranches |
| Round-number stops | Cluster with other traders, get hunted | Offset by small random amount |
| Too-tight stops | Stopped out by normal volatility | Use ATR-based stops |
| Hoping instead of trailing | Gives back profits | Activate trail after first TP |
| Ignoring liquidity | Cannot exit at intended price | Check spread and depth before sizing |

## Integration with Other Skills

- **`position-sizing`** — Size the position based on the stop loss distance.
  `position_size = (account_risk * account_balance) / (entry - stop_loss)`
- **`risk-management`** — Exits are the mechanism that enforces risk limits.
- **`pandas-ta`** — Use ATR, EMA, RSI, MACD for signal-based and trailing exits.
- **`slippage-modeling`** — Estimate execution cost of the exit to set realistic targets.
- **`liquidity-analysis`** — Verify exit liquidity before entering a position.

## Files

### References
- `references/stop_loss_methods.md` — Complete stop loss methodology and anti-patterns
- `references/take_profit_strategies.md` — Scaled exits, R:R targets, Fibonacci extensions
- `references/trailing_stops.md` — Trailing stop implementations and parameter guidance

### Scripts
- `scripts/exit_simulator.py` — Simulate and compare exit strategies on synthetic price data
- `scripts/stop_loss_calculator.py` — Calculate stop levels, position sizes, and R:R targets

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [agiprolabs](https://github.com/agiprolabs)
- **Source:** [agiprolabs/claude-trading-skills](https://github.com/agiprolabs/claude-trading-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.