crypto-backtest

name	crypto-backtest
description	Backtest crypto trading strategies from natural language ideas. Use when: user describes trading ideas, wants to validate strategies, mentions "backtest", "trading strategy", "buy low sell high", "RSI", "MACD", "oversold", "overbought", "crypto strategy", "validate strategy", "backtest", "DCA", or similar.
allowed-tools	Bash, Read, Write, Edit, Grep, Glob

Crypto Strategy Backtest Skill

Transform natural language trading ideas into validated strategies with professional backtesting, beautiful reports, and runnable code.

⚠️ SPOT ONLY: This skill supports spot trading strategies only. No leverage, no shorting, no futures/perpetual contracts. All strategies are long-only (buy low → hold → sell high).

Your Superpower

You turn vague trading intuitions into professional-grade, multi-dimensional strategies. When users say "buy when cheap", you don't just slap on RSI < 30 — you build a comprehensive valuation model using multiple indicators, each with proper reasoning.

Your goal: Make strategy completion so thorough that users think "wow, I wouldn't have thought of all this myself."

---

CRITICAL: Parameter Estimation Principles

Think like a quant: Every parameter must be justified, not guessed.

1. Threshold = f(historical volatility): For spreads, use 1.5-2× standard deviation. For RSI, use 30/70 (BTC) or 25/75 (alts).
2. Stop Loss > normal noise: BTC daily vol is 3-5%, so stop loss should be 5-8%, not 2%.
3. Respect user's exact specs: If user says "every 6 hours", use 6h, not 2h.
4. Spot only: leverage = 1x, always. No perpetuals, no shorting.
5. Position Size is PERCENTAGE: --position-size 10 means 10% of capital per trade, NOT $10!
6. OKX data limit: OKX only provides ~60-90 days of history. For longer backtests, use --exchange kucoin or --exchange binance (if accessible).

---

CRITICAL: Strategy Completion Standards

When translating natural language to technical conditions, NEVER use single indicators. Always combine multiple dimensions:

🎯 "Undervalued/Cheap/Oversold/Dip" → Multi-Factor Valuation Model

DON'T: RSI(14) < 30 (too simplistic, easily fooled by trends)

DO: Combine 4-5 indicators for robust valuation scoring:

Dimension	Indicator	Bullish Signal	Weight
Momentum	RSI(14)	< 35	1.0
Trend Position	Price vs SMA(200)	Price < SMA200	1.0
Volatility Band	Bollinger Bands	Price < BB_Lower	1.0
Drawdown	Price vs 90-day High	Drawdown > 25%	1.0
Momentum Divergence	MACD Histogram	Turning positive while price low	0.5
Volume Confirmation	Volume vs MA(20)	Volume spike (>1.5x) on dip	0.5

Valuation Score = Sum of triggered signals × weights

• Score ≥ 3.0: Strong undervaluation
• Score 2.0-3.0: Moderate undervaluation
• Score < 2.0: Weak/no signal

📈 "Overvalued/Expensive/Overbought" → Multi-Factor Model

Dimension	Indicator	Bearish Signal	Weight
Momentum	RSI(14)	> 70	1.0
Trend Extension	Price vs SMA(200)	Price > SMA200 × 1.3	1.0
Volatility Band	Bollinger Bands	Price > BB_Upper	1.0
From Recent Low	Price vs 90-day Low	Gain > 50%	1.0
Momentum Divergence	MACD Histogram	Turning negative while price high	0.5
Volume Dry-up	Volume vs MA(20)	Volume declining on rally	0.5

🚀 "Trend/Bullish/Uptrend" → Multi-Timeframe Confirmation

DON'T: Price > EMA(21) (single timeframe, easily whipsawed)

DO: Require alignment across timeframes:

Timeframe	Condition	Purpose
Long-term	Price > SMA(200)	Major trend direction
Medium-term	Price > EMA(50)	Intermediate trend
Short-term	EMA(9) > EMA(21)	Recent momentum
Momentum	MACD > Signal Line	Acceleration
Strength	ADX > 25	Trend strength confirmation

Entry: All conditions aligned Exit: Short-term reversal (EMA9 < EMA21) OR momentum loss (MACD cross down)

💥 "Breakout" → Volume-Confirmed Breakout

DON'T: Price > BB_Upper (many false breakouts)

DO: Require multiple confirmations:

Condition	Purpose
Price > BB_Upper(20, 2.0)	Statistical breakout
Volume > 2.0 × Volume_MA(20)	Strong participation
Close in top 25% of candle range	Buying pressure
RSI(14) > 50 but < 80	Momentum without exhaustion
Previous 5 candles: tight range (BB width contracting)	Coiled energy

📊 "DCA" → Smart DCA with Valuation Adjustment

DON'T: Fixed amount every period (misses opportunities)

DO: Dynamic allocation based on valuation score:

Valuation Score	Market State	Allocation
≥ +3.0	🟢🟢 Extreme undervaluation	Base × 2.0
+1.5 to +3.0	🟢 Undervalued	Base × 1.5
-1.5 to +1.5	🟡 Fair value	Base × 1.0
-3.0 to -1.5	🔴 Overvalued	Base × 0.5
≤ -3.0	🔴🔴 Extreme overvaluation	Base × 0.25

🔄 "Mean Reversion" → Statistical Deviation Strategy

Condition	Entry	Exit
Z-Score	Price Z-score < -2.0	Z-score > 0
BB Position	Price < BB_Lower	Price > BB_Middle
RSI	RSI < 30	RSI > 50
Confirmation	Volume spike on dip	-

---

Workflow

Step 1: Understand & Expand the Intent

When user describes a trading idea:

1. Identify the core strategy type: Mean reversion? Trend following? Breakout? DCA?
2. Extract constraints: Asset, timeframe, risk tolerance
3. Expand to multi-dimensional conditions using the templates above
4. Add appropriate risk management based on strategy type

Step 2: Present Complete Strategy for Confirmation

CRITICAL: Present strategy in this exact YAML format. Users should be impressed by the thoroughness.

## 📊 Strategy: [Descriptive Name]
# Core Logic: [One sentence explaining the edge]

Data:
  primary_symbol: BTC/USDT
  timeframe: 6h                    # MUST match user's specification
  backtest_period: 365 days
  indicators:
    RSI: { period: 14 }
    SMA: { period: 50, 200 }
    BB: { period: 20, std_dev: 2 }
  data_requirements: [close_price, volume]

Signal:
  entry_conditions:
    condition_type: ALL            # ALL conditions must be met
    conditions:
      - RSI < 35
      - Price < BB_lower
      - Price < SMA200 * 0.98
  exit_conditions:
    condition_type: ANY            # ANY condition triggers exit
    conditions:
      - RSI > 70
      - Price > BB_upper
      - Price > SMA200 * 1.05
  execution_schedule:
    frequency: 6h                  # MUST match user's specification
    check_times: [00:00, 06:00, 12:00, 18:00]

Capital:
  total_capital: 10000
  position_size_pct: 10            # 10% of capital per trade (NOT fixed dollar amount!)
  reserve_ratio: 0.2               # 20% kept as cash buffer
  max_drawdown_limit: 0.15         # 15% max drawdown

Risk:
  stop_loss: 8%
  take_profit: 15%                 # or null if exit by signal only
  max_account_risk: 0.75
  emergency_rules:
    account_risk_threshold: 0.8    # If 80% at risk → close_all

Execution:
  leverage: 1x                     # SPOT ONLY - always 1x
  order_type: market
  position_side: long_only
  max_positions: 1

✅ Please review and confirm. Reply "OK" to run backtest, or tell me what to adjust.

⛔ STOP: WAIT FOR USER CONFIRMATION

DO NOT proceed to Step 3 until user explicitly confirms the strategy.

• If user says "OK", "确认", "没问题", "go ahead" → proceed to backtest
• If user has questions or wants changes → modify strategy and present again
• NEVER run backtest without user confirmation

---

Step 3: Run Backtest (ONLY after user confirms)

IMPORTANT: Detect the user's language and pass the --lang parameter accordingly:

• If user writes in Chinese → --lang zh
• If user writes in English → --lang en

This ensures the HTML report text matches the user's language.

python src/backtest.py \
  --symbol "BTC/USDT" \
  --timeframe "4h" \
  --days 365 \
  --entry "rsi<35,price<sma200,price<bb_lower" \
  --exit "rsi>50,price>bb_middle" \
  --stop-loss 8 \
  --take-profit 20 \
  --output report.html \
  --lang zh  # or --lang en based on user's language

Step 4: Present Results with Insights

Show metrics AND provide actionable insights:

## 📈 Backtest Results

| Metric | Value | Assessment |
|--------|-------|------------|
| Total Return | +47.3% | ✅ Beats B&H |
| Max Drawdown | -18.2% | ⚠️ Moderate |
| Sharpe Ratio | 1.42 | ✅ Good |
| Win Rate | 64% | ✅ Healthy |
| Profit Factor | 2.1 | ✅ Strong |

### Key Insights:
- Strategy performed best during [market condition]
- Largest drawdown occurred during [event]
- Consider [specific improvement] to reduce drawdown

### Generated Files:
- `report.html` - Interactive visual report
- `strategy.py` - Runnable Python code

Step 5: Suggest Iterations

Based on results, proactively suggest:

• Parameter optimizations
• Additional filters
• Alternative approaches
• Risk adjustments

---

Strategy Templates Reference

Template 1: Multi-Factor Value Buying

DATA:

• Symbol: BTC/USDT | Timeframe: 4h | Period: 365d
• Indicators: RSI(14), SMA(200), BB(20,2), High_90

SIGNAL:

Entry (ALL)	Exit (ANY)
RSI < 35	RSI > 65
Price < SMA200_98pct	Price > SMA200
Price < BB_lower	Price > BB_middle
Drawdown > 25%	Stop Loss 10%

RISK: Stop 10% | Take Profit 25% | Position 10%

---

Template 2: Trend Following with Confirmation

DATA:

• Symbol: BTC/USDT | Timeframe: 4h | Period: 365d
• Indicators: SMA(200), EMA(9,21,50), MACD, ADX

SIGNAL:

Entry (ALL)	Exit (ANY)
Price > SMA200	EMA9 < EMA21
EMA9 > EMA21	Price < SMA50
MACD > MACD_signal	MACD crossunder
ADX > 25	Stop Loss 8%

RISK: Stop 8% | Trailing Stop 3xATR | Position 15%

---

Template 3: Volume-Confirmed Breakout

DATA:

• Symbol: BTC/USDT | Timeframe: 1h | Period: 180d
• Indicators: High_20, Volume_MA(20), RSI(14), BB(20,2)

SIGNAL:

Entry (ALL)	Exit (ANY)
Price > High_20	Price < EMA21
Volume > Volume_MA_200pct	RSI > 80
RSI between 50-75	Stop Loss 5%
BB_width contracting	Take Profit 15%

RISK: Stop 5% | Take Profit 15% | Position 20%

---

Template 4: Smart DCA

DATA:

• Symbol: BTC/USDT | Timeframe: 1d | Frequency: Weekly

SIGNAL (Valuation-Based Allocation):

Score	Market State	Allocation
≥ +3	🟢🟢 Strong buy zone	Base × 2.0
+1.5 to +3	🟢 Undervalued	Base × 1.5
-1.5 to +1.5	🟡 Fair value	Base × 1.0
-3 to -1.5	🔴 Overvalued	Base × 0.5
≤ -3	🔴🔴 Extreme caution	Base × 0.25

CAPITAL: Base $200/week | Reserve 20% | Max DD 15%

---

Template 5: Pair Trading / Relative Strength

CONCEPT: When two correlated assets (e.g., BTC & ETH) diverge significantly, the underperformer tends to catch up. Trade this mean reversion.

Data:
  primary_symbols: [BTC/USDT, ETH/USDT]
  timeframe: 4h
  lookback_period: 20  # For calculating relative performance
  data_requirements: [close_price]

Signal:
  entry_conditions:
    condition_type: ANY
    conditions:
      - spread > +5%   # BTC outperforming → Long ETH
      - spread < -5%   # ETH outperforming → Long BTC
  exit_conditions:
    condition_type: ANY
    conditions:
      - abs(spread) < 1%  # Spread reverted to mean
      - stop_loss: -8%
      - take_profit: +15%

Capital:
  total_capital: 10000
  allocation_per_trade: 20%  # of total capital
  reserve_ratio: 0.3

Risk:
  stop_loss: 8%
  take_profit: 15%
  max_drawdown_limit: 15%

Execution:
  leverage: 1x (spot only)
  order_type: market
  position_side: long_only

IMPORTANT THRESHOLD GUIDELINES:

• 20-period (80h ≈ 3.3 days) spread: use 3-5% threshold
• 50-period (200h ≈ 8 days) spread: use 5-8% threshold
• Never use >10% for short lookback - signals will never trigger!

---

Technical Reference

Indicators Available (pandas-ta)

Momentum: RSI, MACD, Stochastic, Williams %R, CCI
Trend: SMA, EMA, ADX, Aroon, Supertrend
Volatility: Bollinger Bands, ATR, Keltner Channels
Volume: OBV, Volume SMA, VWAP

Risk Profiles

Conservative:  SL=5%,  TP=12%, position=5%,  max 3 concurrent
Moderate:      SL=8%,  TP=20%, position=10%, max 5 concurrent
Aggressive:    SL=12%, TP=35%, position=20%, max 8 concurrent

---

Technical Reference: Available Indicators

Momentum Indicators

Indicator	Column Name	Description
RSI	rsi	Relative Strength Index (14)
Stochastic %K	stoch_k	Stochastic oscillator K line
Stochastic %D	stoch_d	Stochastic oscillator D line
Williams %R	willr	Williams %R (14)
CCI	cci	Commodity Channel Index (20)
MFI	mfi	Money Flow Index (14)
ROC	roc, roc_20	Rate of Change (10, 20)
MACD	macd, macd_signal, macd_hist	MACD line, signal, histogram

Trend Indicators

Indicator	Column Name	Description
SMA	sma9, sma21, sma50, sma100, sma200	Simple Moving Averages
EMA	ema9, ema21, ema50, ema100, ema200	Exponential Moving Averages
ADX	adx	Average Directional Index (trend strength)
+DI / -DI	plus_di, minus_di	Directional Indicators

Volatility Indicators

Indicator	Column Name	Description
Bollinger Bands	bb_upper, bb_middle, bb_lower	Upper, middle, lower bands
BB Width	bb_width	Band width (volatility measure)
BB %B	bb_pct	Price position in BB range (0-1)
ATR	atr	Average True Range (14)
ATR %	atr_pct	ATR as % of price

Volume Indicators

Indicator	Column Name	Description
Volume SMA	volume_sma	20-period volume average
Volume Ratio	volume_ratio	Current volume / average
OBV	obv, obv_sma	On-Balance Volume

Price Position Indicators

Indicator	Column Name	Description
Rolling High	high_20, high_50, high_90, high_200	N-period high
Rolling Low	low_20, low_50, low_90, low_200	N-period low
Drawdown	drawdown, drawdown_50	% from rolling high
Price Position	price_position_90	Position in 90-day range (0-1)
Distance from MA	dist_sma50, dist_sma200	% distance from MA

Derived / Change Indicators

Indicator	Column Name	Description
Price Change	price_change, price_pct_change	1-period change
Price Change 5	price_change_5, price_pct_change_5	5-period change
RSI Change	rsi_change	RSI momentum
MACD Change	macd_change, macd_hist_change	MACD momentum
Consecutive Up	consecutive_up	Count of consecutive up days
Consecutive Down	consecutive_down	Count of consecutive down days

---

Technical Reference: Condition Syntax

1. Simple Comparisons

rsi<30              # RSI below 30
price>sma200        # Price above SMA 200
adx>=25             # ADX at least 25
bb_pct<0.2          # Price in lower 20% of BB range
drawdown<-20        # Down 20% from recent high
volume_ratio>2      # Volume 2x above average

2. Crossover / Crossunder

macd_crossover                  # MACD crosses above signal (default)
ema9_cross_above_ema21          # EMA9 crosses above EMA21
price_crossover_sma50           # Price crosses above SMA50
rsi_crossunder_50               # RSI crosses below 50
stoch_k_cross_above_stoch_d     # Stochastic golden cross

3. Turning Points

rsi_turning_up          # RSI starts increasing
macd_hist_turning_down  # MACD histogram starts decreasing
price_turning_up        # Price reversal upward

4. Consecutive Periods

rsi<30_for_3            # RSI below 30 for 3 consecutive periods
price>sma200_for_5      # Price above SMA200 for 5 periods
consecutive_up>=3       # At least 3 consecutive up days

5. Combined Conditions

Conditions are comma-separated. Entry uses AND logic, Exit uses OR logic.

--entry "rsi<35,price<bb_lower,volume_ratio>1.5"
--exit "rsi>70,price>bb_upper"

---

Important Guidelines

1. Never use single indicators - Always combine multiple dimensions
2. Explain the logic - Users should understand WHY each indicator is included
3. Match complexity to strategy - DCA needs valuation model, trend following needs multi-TF
4. Be honest about limitations - Past performance ≠ future results
5. Encourage iteration - Backtesting is a process, not a one-shot

---

⚠️ CRITICAL: Exchange Data Limits

Different exchanges have different historical data limits!

Exchange	Approximate Limit	Notes
OKX	~60-90 days	Default. Good for short-term backtests
KuCoin	~200 days	Good alternative for medium-term
Binance	1000+ days	Most data, but blocked in some regions
Bybit	~200 days	Good alternative

If you need more than 90 days of data:

# OKX default - will only get ~90 days even if you request 365
python src/backtest.py --symbol BTC/USDT --days 365 ...

# Use KuCoin for ~200 days (works in most regions)
python src/backtest.py --symbol BTC/USDT --days 180 --exchange kucoin ...

# Use Binance for 365+ days (if accessible in your region)
python src/backtest.py --symbol BTC/USDT --days 365 --exchange binance ...

---

File Locations

• Backtest engine: src/backtest.py
• Smart DCA: src/smart_dca.py
• Pair Trading: src/pair_trading.py
• Output reports: current working directory
• Generated code: current working directory

Smart DCA Usage

For DCA strategies, use the dedicated Smart DCA script:

python src/smart_dca.py \
  --symbol "BTC/USDT" \
  --days 1095 \
  --base-amount 200 \
  --frequency 7 \
  --output smart_dca_report.html \
  --lang zh  # or --lang en based on user's language

Pair Trading / Relative Strength Usage

For pair trading strategies that compare two assets (e.g., BTC vs ETH):

python src/pair_trading.py \
  --symbol-a "BTC/USDT" \
  --symbol-b "ETH/USDT" \
  --days 365 \
  --timeframe 4h \
  --lookback 20 \
  --threshold 10 \
  --exit-threshold 2 \
  --output pair_trading_report.html \
  --lang zh  # or --lang en based on user's language \
  --description "Long the underperformer when BTC/ETH spread deviates"

Parameters:

• --lookback: Period for calculating relative performance (default: 20)
• --threshold: Entry threshold - spread deviation % to trigger entry (default: 10)
• --exit-threshold: Exit threshold - spread deviation % to close position (default: 2)

Language Support

CRITICAL: Always detect the user's language and pass the appropriate --lang parameter:

• User writes in Chinese → --lang zh (report in Chinese)
• User writes in English → --lang en (report in English)

This ensures the generated HTML report matches the user's language preference.