Strategy Templates

Three representative strategy patterns used with AlphaForge. The strategy JSON is fully copy-pasteable and can be registered as-is via forge strategy save.

About sample output

Backtest result numbers are illustrative. Actual values depend on data and environment (fetch period, provider, settings).

Templates covered

Template	Strategy type	Key indicators
HMM × BB × RSI	Regime-adaptive mean reversion	HMM / BBANDS / RSI
Regime switching	Per-regime strategy switching	HMM / SUPERTREND / BBANDS / RSI
Multi-timeframe	Higher-TF trend × daily entry	Weekly SMA / RSI / ATR

Strategy JSON basics

Every strategy follows the same Pydantic schema. Indicator details are available via forge analyze indicator list.

{
  "strategy_id": "...",
  "name": "...",
  "target_symbols": [...],
  "asset_type": "stock",
  "timeframe": "1d",
  "parameters": {...},          // Top-level parameters subject to optimization
  "indicators": [...],          // Computed indicators (30+ types)
  "variables": [...],           // Intermediate boolean variables (optional)
  "entry_conditions": {...},    // Entry conditions
  "exit_conditions": {...},     // Exit conditions
  "risk_management": {...},     // Position size, SL/TP
  "regime_config": {...},       // Regime adaptation (optional)
  "optimizer_config": {...}     // Optimization parameter ranges
}

Key concepts:

• indicators[].lock_on_entry: true — Freeze the value at the entry bar (used for SL/TP prices)
• indicators[].timeframe: "1w" — Pull values from a higher timeframe (multi-timeframe)
• EXPR indicator — Arbitrary pandas expression (e.g., "close * 0.98")
• HMM indicator — Hidden Markov Model regime detection
• regime_config — Switch entry/exit/risk_override per regime, keyed on the HMM output

---

HMM × BB × RSI

Overview

Mean-reversion entries triggered by Bollinger Band lower break + RSI oversold, filtered through a 3-state HMM regime detector. Bull/Neutral/Bear regimes are distinguished, with aggressive leverage in Bull (5x), conservative in Neutral (3x), and skip in Bear.

The strength of this template is managing three regimes within a single strategy JSON. A mean-reversion strategy that works in range markets is automatically muted (or fully paused) when trends are too strong or too weak — keeping drawdowns under control.

Suitable scenarios

• Symbols: US large-cap ETFs (QQQ, SPY), growth stocks (NVDA), instruments with low long-horizon transaction costs
• Market environment: Mid-to-long-term regimes alternating between trend and range (like 2018-2025)
• Holding period: Days to ~2 weeks
• Risk profile: Leverage-friendly (up to 5x); HMM mis-detection can amplify drawdowns

Strategy JSON (full)

{
  "strategy_id": "multi_asset_hmm_bb_rsi_v1_qqq",
  "name": "Multi-Asset HMM×BB+RSI v1 (QQQ)",
  "version": "1.0.0",
  "description": "HMM 3-state regime filter × BB+RSI mean reversion. Bull(state=0): long on BB-lower + RSI oversold (leverage=3). Neutral(state=1): same condition (leverage=1.5). Bear(state=2): skip. Daily.",
  "target_symbols": ["QQQ"],
  "asset_type": "stock",
  "timeframe": "1d",
  "parameters": {
    "rsi_oversold_th": 35,
    "atr_mult": 2.0
  },
  "indicators": [
    { "id": "regime",   "type": "HMM",     "params": { "n_components": 3, "features": ["return", "volatility"] } },
    { "id": "bb_lower", "type": "BBANDS",  "params": { "length": 20, "std": 2.0, "line": "lower" } },
    { "id": "bb_mid",   "type": "BBANDS",  "params": { "length": 20, "std": 2.0, "line": "mid" } },
    { "id": "rsi",      "type": "RSI",     "params": { "length": 7 } },
    { "id": "atr",      "type": "ATR",     "params": { "length": 14 } },
    { "id": "sl_dist",  "type": "EXPR",    "params": { "expr": "atr * atr_mult" }, "lock_on_entry": true }
  ],
  "variables": [],
  "entry_conditions": { "long": { "logic": "AND", "conditions": [] } },
  "exit_conditions":  { "long": { "logic": "OR",  "conditions": [] } },
  "risk_management": {
    "leverage": 5.0,
    "position_sizing_method": "fixed",
    "position_size_pct": 100.0,
    "stop_loss_indicator": "sl_dist",
    "max_positions": 1
  },
  "regime_config": {
    "indicator_id": "regime",
    "default_action": "skip",
    "states": {
      "0": {
        "entry_conditions": { "long": { "logic": "AND", "conditions": [
          { "left": "close", "op": "<", "right": "bb_lower" },
          { "left": "rsi",   "op": "<", "right": "rsi_oversold_th" }
        ]}},
        "exit_conditions":  { "long": { "logic": "OR",  "conditions": [
          { "left": "close", "op": ">", "right": "bb_mid" }
        ]}},
        "risk_override": { "leverage": 5.0, "position_sizing_method": "fixed", "position_size_pct": 100.0 }
      },
      "1": {
        "entry_conditions": { "long": { "logic": "AND", "conditions": [
          { "left": "close", "op": "<", "right": "bb_lower" },
          { "left": "rsi",   "op": "<", "right": "rsi_oversold_th" }
        ]}},
        "exit_conditions":  { "long": { "logic": "OR",  "conditions": [
          { "left": "close", "op": ">", "right": "bb_mid" }
        ]}},
        "risk_override": { "leverage": 3.0, "position_sizing_method": "fixed", "position_size_pct": 100.0 }
      },
      "2": {}
    }
  },
  "backtest_config": {
    "regime_analysis": {
      "method": "hmm",
      "hmm_indicator_id": "regime",
      "label_names": { "0": "Bull", "1": "Neutral", "2": "Bear" }
    }
  },
  "optimizer_config": {
    "param_ranges": {
      "bb_lower.length": { "min": 15,  "max": 25,  "step": 1 },
      "bb_lower.std":    { "min": 1.8, "max": 2.5, "step": 0.1 },
      "rsi.length":      { "min": 5,   "max": 14,  "step": 1 },
      "rsi_oversold_th": { "min": 25,  "max": 45,  "step": 5 },
      "atr_mult":        { "min": 1.5, "max": 3.0, "step": 0.5 }
    },
    "constraints": { "min_trades": 20 },
    "metric": "sharpe_ratio"
  },
  "tags": ["hmm", "bb", "rsi", "mean-reversion", "leverage", "nas100"]
}

Key parameters

Parameter	Role	Recommended range
regime.n_components	HMM state count	3 (Bull/Neutral/Bear)
regime.features	HMM input features	["return", "volatility"]
bb_lower.length / std	BB period and std multiplier	period 15-25, std 1.8-2.5
rsi.length	RSI period	5-14 (shorter = more responsive)
rsi_oversold_th	Entry threshold	25-45 (lower = stricter)
atr_mult	ATR-based SL multiplier	1.5-3.0
risk_override.leverage	Per-regime leverage	Bull 5.0 / Neutral 3.0 / Bear 0 (skip)

Sample backtest output

Sample output

Numbers depend on data and environment.

==> QQQ 2018-01-01 → 2025-12-31 (1d)
   trades: 38   win_rate: 65.8%   profit_factor: 2.15
   total_return: +124.5%   cagr: +10.7%   sharpe: 1.42
   max_drawdown: -18.4%   exposure: 24.3%
   final_equity: $22,450  (initial: $10,000)

Customization tips

• Change the symbol: Replace target_symbols with ["SPY"], ["NVDA"], ["GC=F"], etc.
• Change state count: regime.n_components: 2 (Bull/Bear) simplifies the decision; 4 adds nuance (requires more data)
• Strengthen entry: Add volume > sma_volume_20 or similar conditions in regime_config.states["0"].entry_conditions
• Optimize: forge optimize run QQQ --strategy multi_asset_hmm_bb_rsi_v1_qqq --metric sharpe_ratio --save

---

Regime switching

Overview

A pattern that applies different strategies per regime, keyed on HMM output. Within a single strategy JSON, the Bull regime runs trend-following and the Bear/Range regime runs mean reversion — swapping the strategy itself rather than just parameters.

The defining feature versus HMM × BB × RSI: regime_config.states lets you define entry_conditions and exit_conditions completely independently per regime.

Suitable scenarios

• Symbols: Commodity futures (CL=F, GC=F, NG=F) — high-volatility instruments with clear trend/range alternation
• Market environment: Capture both trend and counter-trend behavior typical of commodity markets
• Holding period: Days to a month
• Risk profile: High leverage (10x) + ATR-based SL; assumes margin-based commodity trading

Strategy JSON (full)

{
  "strategy_id": "commodity_hmm_regime_v1",
  "name": "Commodity HMM Regime v1",
  "version": "1.0.0",
  "description": "Regime-adaptive for commodity CFDs: HMM 2-state Bull/Bear. Bull = SuperTrend long, Bear = BB+RSI mean reversion. leverage=10.",
  "target_symbols": ["GC=F", "SI=F", "CL=F", "BZ=F", "NG=F", "ZC=F", "ZS=F", "ZW=F", "HG=F"],
  "asset_type": "stock",
  "timeframe": "1d",
  "parameters": {
    "adx_threshold": 20,
    "rsi_threshold": 35,
    "atr_mult": 2.0
  },
  "indicators": [
    { "id": "regime",         "type": "HMM",        "params": { "n_components": 2, "features": ["return", "volatility"], "volatility_window": 10 } },
    { "id": "supertrend_val", "type": "SUPERTREND", "params": { "length": 9, "multiplier": 3.0 } },
    { "id": "adx_val",        "type": "ADX",        "params": { "length": 14 } },
    { "id": "bb_lower",       "type": "BBANDS",     "params": { "length": 20, "std": 2.0, "line": "lower" } },
    { "id": "bb_mid",         "type": "BBANDS",     "params": { "length": 20, "std": 2.0, "line": "mid" } },
    { "id": "rsi",            "type": "RSI",        "params": { "length": 14 } },
    { "id": "atr",            "type": "ATR",        "params": { "length": 14 } },
    { "id": "sl_dist",        "type": "EXPR",       "params": { "expr": "atr * atr_mult" }, "lock_on_entry": true }
  ],
  "variables": [],
  "entry_conditions": { "long": { "logic": "AND", "conditions": [] } },
  "exit_conditions":  { "long": { "logic": "OR",  "conditions": [] } },
  "risk_management": {
    "leverage": 10.0,
    "position_sizing_method": "risk_based",
    "risk_per_trade_pct": 1.5,
    "stop_loss_indicator": "sl_dist",
    "max_positions": 1
  },
  "regime_config": {
    "indicator_id": "regime",
    "states": {
      "0": {
        "entry_conditions": { "long": { "logic": "AND", "conditions": [
          { "left": "close",   "op": "crosses_above", "right": "supertrend_val" },
          { "left": "adx_val", "op": ">",             "right": "adx_threshold" }
        ]}},
        "exit_conditions":  { "long": { "logic": "OR",  "conditions": [
          { "left": "close", "op": "crosses_below", "right": "supertrend_val" }
        ]}}
      },
      "1": {
        "entry_conditions": { "long": { "logic": "AND", "conditions": [
          { "left": "close", "op": "<", "right": "bb_lower" },
          { "left": "rsi",   "op": "<", "right": "rsi_threshold" }
        ]}},
        "exit_conditions":  { "long": { "logic": "OR",  "conditions": [
          { "left": "close", "op": ">", "right": "bb_mid" }
        ]}}
      }
    }
  },
  "optimizer_config": {
    "param_ranges": {
      "supertrend_val.multiplier": { "min": 2.0, "max": 4.0, "step": 0.5 },
      "adx_threshold":             { "min": 15,  "max": 30,  "step": 5 },
      "rsi_threshold":             { "min": 25,  "max": 45,  "step": 5 },
      "atr_mult":                  { "min": 1.5, "max": 3.0, "step": 0.5 }
    },
    "constraints": { "min_trades": 15 },
    "metric": "sharpe_ratio"
  },
  "tags": ["hmm", "regime", "adaptive", "commodity", "leverage-10"]
}

Key parameters

Parameter	Role	Recommended range
regime.n_components	HMM state count	2 (simple Bull/Bear switch)
regime.volatility_window	Volatility computation window	10 (short) to 30 (mid)
supertrend_val.multiplier	SuperTrend channel width	2.0-4.0 (smaller = more responsive)
adx_threshold	Trend strength threshold	15-30 (≥25 = strong trend)
rsi_threshold	Mean-reversion oversold threshold	25-45
risk_per_trade_pct	% risk per trade	1.5 (conservative)
leverage	Commodity futures leverage	10 (margin trading assumed)

Sample backtest output

Sample output

==> CL=F 2018-01-01 → 2025-12-31 (1d)
   trades: 27   win_rate: 51.9%   profit_factor: 1.82
   total_return: +88.3%   cagr: +8.4%   sharpe: 1.21
   max_drawdown: -22.1%   exposure: 31.5%
   regime_breakdown: state=0 (Bull): 14 trades, sharpe 1.45  /  state=1 (Bear): 13 trades, sharpe 0.92

Customization tips

• Add more states: n_components: 3 for Bull/Range/Bear; add states["2"]
• Switch to equities: Replace target_symbols with stocks and lower risk_management.leverage to 1.0-2.0
• More entries: Loosen each regime's entry_conditions (e.g., adx_threshold: 15, rsi_threshold: 45)
• Cross-symbol optimize: forge optimize cross-symbol GC=F SI=F CL=F --strategy commodity_hmm_regime_v1 --aggregation min --save

---

Multi-timeframe

Overview

Use the indicators[].timeframe field to pull higher-timeframe values while entering on the lower timeframe. Judge a long-term trend on the weekly SMA and time pullback entries on the daily RSI.

Educational example

The strategy JSON in this section is written as an example of the indicators[].timeframe feature. Validate behavior with forge strategy validate and forge backtest run before live use.

Suitable scenarios

• Symbols: US large-cap stocks / ETFs (SPY, QQQ, AAPL)
• Market environment: Long-term uptrend with short-term pullbacks
• Holding period: 1 day to 2 weeks
• Risk profile: Trend-following design — watch for drawdowns at trend reversals. Skips entries entirely when the weekly trend turns down.

Strategy JSON (full)

{
  "strategy_id": "spy_mtf_trend_pullback_v1",
  "name": "SPY Multi-Timeframe Trend Pullback v1",
  "version": "1.0.0",
  "description": "Multi-timeframe strategy: judge trend with weekly SMA, time pullback entries on daily RSI oversold.",
  "target_symbols": ["SPY"],
  "asset_type": "stock",
  "timeframe": "1d",
  "parameters": {
    "rsi_oversold_th": 35,
    "atr_mult": 2.0
  },
  "indicators": [
    {
      "id": "weekly_sma",
      "type": "SMA",
      "params": { "length": 20 },
      "source": "close",
      "timeframe": "1w"
    },
    {
      "id": "weekly_close",
      "type": "EXPR",
      "params": { "expr": "close" },
      "timeframe": "1w"
    },
    { "id": "rsi", "type": "RSI", "params": { "length": 7 } },
    { "id": "sma_50", "type": "SMA", "params": { "length": 50 } },
    { "id": "atr", "type": "ATR", "params": { "length": 14 } },
    { "id": "sl_dist", "type": "EXPR", "params": { "expr": "atr * atr_mult" }, "lock_on_entry": true }
  ],
  "variables": [
    {
      "id": "weekly_uptrend",
      "logic": "AND",
      "conditions": [
        { "left": "weekly_close", "op": ">", "right": "weekly_sma" }
      ]
    }
  ],
  "entry_conditions": {
    "long": {
      "logic": "AND",
      "conditions": [
        { "left": "weekly_uptrend", "op": "==", "right": true },
        { "left": "close", "op": ">", "right": "sma_50" },
        { "left": "rsi",   "op": "<", "right": "rsi_oversold_th" }
      ]
    }
  },
  "exit_conditions": {
    "long": {
      "logic": "OR",
      "conditions": [
        { "left": "rsi", "op": ">", "right": 60 },
        { "left": "close", "op": "<", "right": "sma_50" }
      ]
    }
  },
  "risk_management": {
    "leverage": 1.0,
    "position_sizing_method": "fixed",
    "position_size_pct": 25.0,
    "stop_loss_indicator": "sl_dist",
    "max_positions": 1
  },
  "regime_config": null,
  "optimizer_config": {
    "param_ranges": {
      "weekly_sma.length": { "min": 10, "max": 30, "step": 5 },
      "rsi.length":        { "min": 5,  "max": 14, "step": 1 },
      "rsi_oversold_th":   { "min": 25, "max": 45, "step": 5 },
      "atr_mult":          { "min": 1.5, "max": 3.0, "step": 0.5 }
    },
    "constraints": { "min_trades": 20 },
    "metric": "sharpe_ratio"
  },
  "tags": ["multi-timeframe", "trend-following", "pullback", "weekly", "spy"]
}

Key parameters

Parameter	Role	Recommended range
weekly_sma.timeframe: "1w"	Higher timeframe for the SMA	"1w" (also "4h", "1mo")
weekly_sma.length	Weekly SMA period	10-30 (mid-to-long trend)
rsi.length	Daily RSI period	5-14
rsi_oversold_th	Pullback threshold	25-45
sma_50	Daily mid-trend filter	fixed 50
position_size_pct	Per-position size	25% (conservative)

The indicators[].timeframe field computes only that indicator on a different timeframe. When a daily-base strategy (timeframe: "1d") references a weekly indicator, the matching weekly value is automatically forward-filled (ffill) to align with each daily row.

Sample backtest output

Sample output

==> SPY 2018-01-01 → 2025-12-31 (1d)
   trades: 32   win_rate: 62.5%   profit_factor: 1.95
   total_return: +68.2%   cagr: +6.7%   sharpe: 1.18
   max_drawdown: -14.3%   exposure: 28.7%

Customization tips

• Change the higher timeframe: weekly_sma.timeframe: "4h" for intraday MTF; "1mo" for monthly-led strategies
• Combine multiple higher timeframes: Require both weekly and monthly SMAs to be cleared before entering
• Expand symbols: target_symbols: ["SPY", "QQQ", "DIA"] and use forge optimize cross-symbol for robust parameters
• Add shorts: Define entry_conditions.short for "weekly downtrend + daily RSI overbought"

---

Customization and derivations

Parameter optimization

Each template includes optimizer_config.param_ranges, so Optuna Bayesian optimization runs with:

forge optimize run <SYMBOL> --strategy <STRATEGY_ID> --metric sharpe_ratio --save

See forge optimize run for details.

Walk-forward to guard against overfitting

forge optimize walk-forward <SYMBOL> --strategy <STRATEGY_ID> --windows 5

Each window runs IS optimization → OOS evaluation; check overfitting_score afterwards.

Sensitivity analysis to measure robustness

forge optimize sensitivity <RESULT_FILE>

Sweep around optimized parameters and measure how much the metric moves. If overall_robustness_score ≤ 0.7, suspect overfitting.

Compare with live results

Once trade records accumulate, compare against backtest:

forge live compare <STRATEGY_ID>

See forge live compare.

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Related documentation

• Getting Started — Start with a simple SMA crossover
• CLI Reference — All forge command parameters
• AI-Driven Strategy Exploration Workflow — Generate strategies with Claude Code / Codex

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