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 <= stop

ATR trailing (Chandelier Exit):

def chandelier_exit(
    highs: list[float],
    atr_value: float,
    multiplier: float = 2.5,
    lookback: int = 22,
) -> float:
    """Highest high over lookback minus ATR * multiplier."""
    highest_high = max(highs[-lookback:])
    return highest_high - (atr_value * multiplier)

EMA trailing:

# Exit when close < EMA for M consecutive bars
ema = df.ta.ema(length=20)
below_ema = df["close"] < ema
consecutive_below = below_ema.rolling(3).sum() == 3  # 3 bars below

Typical EMA periods: 10 (scalp), 20 (day trade), 50 (swing).

See references/trailing_stops.md for Parabolic SAR, SuperTrend, and step trailing.

4. Time-Based Exits

Exit if the trade hasn't moved in your favor within a defined window.

bars_since_entry = current_bar - entry_bar
if bars_since_entry > max_hold_bars and current_pnl <= 0:
    exit_reason = "time_stop"

Guidelines:

• Scalp: 5–15 minutes
• Day trade: 4–8 hours
• Swing: 3–5 days
• PumpFun snipe: 2–10 minutes (token-specific)

Time stops prevent capital from sitting in dead trades.

5. Signal-Based Exits

Exit when the indicator that generated the entry signal reverses.

# RSI reversal exit
rsi = df.ta.rsi(length=14)
if position == "long" and rsi.iloc[-1] > 70:
    exit_reason = "rsi_overbought"

# MACD crossover exit
macd = df.ta.macd()
if macd["MACDs_12_26_9"].iloc[-1] < macd["MACDh_12_26_9"].iloc[-1]:
    exit_reason = "macd_bearish_cross"

Signal exits work well when combined with trailing stops — the signal triggers tightening the trail rather than an immediate full exit.

6. Liquidity-Based Exits

Exit when volume or liquidity deteriorates, signaling reduced ability to exit cleanly.

recent_vol = df["volume"].rolling(10).mean().iloc[-1]
baseline_vol = df["volume"].rolling(50).mean().iloc[-1]

if recent_vol < baseline_vol * 0.3:  # Volume dropped to 30% of baseline
    exit_reason = "liquidity_deterioration"

Critical for low-cap Solana tokens where liquidity can evaporate rapidly.

PumpFun-Specific Exit Rules

PumpFun tokens have unique dynamics requiring specialized exit logic.

Pre-Graduation Exits

Tokens on the bonding curve before reaching 85 SOL fill:

bonding_fill_pct = current_fill_sol / 85.0

if bonding_fill_pct > 0.90:
    # Near graduation — decide: hold through or exit before
    # Graduation creates volatility spike, both up and down
    pass

if bonding_fill_pct < 0.50 and time_since_entry > 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 < buy_vol_5m * 0.3:
    exit_reason = "buy_volume_decay"

Time Decay for PumpFun

Most PumpFun tokens that will succeed show momentum within the first few minutes:

Timeframe	Action
0–2 min	Hold — too early to judge
2–5 min	Exit if no 2× from entry
5–10 min	Exit if no 3× from entry
10+ min	Should be trailing, not hoping

Combining Exit Rules

A complete exit plan layers multiple rules. Here is a recommended template:

exit_plan = {
    "hard_stop": {
        "type": "fixed_percentage",
        "value": 0.20,  # -20% max loss
        "priority": 1,   # Checked first, always honored
    },
    "atr_stop": {
        "type": "atr_trailing",
        "multiplier": 2.5,
        "atr_length": 14,
        "priority": 2,
    },
    "take_profit": {
        "type": "scaled",
        "tranches": [
            {"at_rr": 2, "sell_pct": 0.25},
            {"at_rr": 4, "sell_pct": 0.25},
            {"at_rr": 8, "sell_pct": 0.25},
        ],
        "priority": 3,
    },
    "time_stop": {
        "type": "max_bars",
        "value": 50,
        "condition": "if_not_profitable",
        "priority": 4,
    },
}

Priority hierarchy: Hard stop > 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