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Strategy Authoring Guide

manifoldbt — Python DSL for Declarative Strategy Definition

This guide describes how to define trading strategies using the manifoldbt Python DSL. Strategies are compiled into an optimized expression graph and executed by the Rust vectorized engine.

Quick Start
Indicators
Signals & Sizing
Parameters & Sweeps
Backtest Configuration
Execution Model
Fee & Slippage Models
Orders (SL/TP/Trailing)
Entry Orders
Cross-Asset References
Dataset Auto-Resolution
Diagnostics
Profiling
Complete Examples
Indicator Reference

Quick Start

import manifoldbt as mbt
from manifoldbt.indicators import close, ema
from manifoldbt.helpers import time_range, Slippage, Interval

# -- Indicators
fast = ema(close, 12)
slow = ema(close, 50)

# -- Strategy
strategy = (
    mbt.Strategy.create("ema_cross")
    .signal("fast", fast)
    .signal("slow", slow)
    .size(mbt.when(fast > slow, 0.5, 0.0))
    .stop_loss(pct=3.0)
)

# -- Config
start, end = time_range("2022-01-01", "2025-01-01")
config = mbt.BacktestConfig(
    universe=[1],
    time_range_start=start,
    time_range_end=end,
    bar_interval=Interval.hours(12),
    initial_capital=10_000,
    fees=mbt.FeeConfig.binance_perps(),
    slippage=Slippage.fixed_bps(2),
    warmup_bars=60,
)

# -- Run
store = mbt.DataStore(data_root="data", metadata_db="metadata/metadata.sqlite")
result = mbt.run(strategy, config, store)
print(result.summary())

Indicators

All indicators are available from manifoldbt.indicators. They return Expr objects that compose into the expression graph — no data is touched at definition time.

from manifoldbt.indicators import (
    close, open, high, low, volume,  # price columns
    ema, sma, dema, tema, wma, hma, kama,  # moving averages
    rsi, roc, momentum, macd,  # momentum
    bollinger_bands, atr, natr, keltner_channels,  # volatility
    stoch_k, williams_r, cci, adx,  # oscillators
    obv, vwap, mfi,  # volume
    kalman, garch,  # filters
)

Usage

fast = ema(close, 12)          # EMA with span 12
slow = sma(close, 50)          # SMA with window 50
strength = rsi(close, 14)      # RSI with period 14
upper, mid, lower = bollinger_bands(close, period=20, num_std=2.0)

Method chaining

Column expressions (close, high, etc.) support method chaining:

zscore = close.zscore(60)           # rolling z-score
slope = close.linreg_slope(20)      # linear regression slope
smoothed = close.ewm_mean(12)       # EMA
lagged = close.lag(5)               # 5-bar lag
ret = close.pct_change(1)           # 1-bar return

Signals & Sizing

Strategy builder

strategy = (
    mbt.Strategy.create("my_strategy")
    .signal("fast", fast)           # named signal
    .signal("slow", slow)           # signals form a DAG
    .size(signal_expr)              # position sizing expression
    .describe("Strategy description")
)

`mbt.when()` — conditional logic

# Long when fast > slow, flat otherwise
signal = mbt.when(fast > slow, 0.5, 0.0)

# Nested: long / short / flat
signal = mbt.when(fast > slow, 0.25,
         mbt.when(fast < slow, -0.25, 0.0))

# Hold current position (omit 3rd arg or use NaN)
signal = mbt.when(rsi < 30, 1.0)  # buy oversold, hold otherwise

Arithmetic on expressions

trend = fast - slow
spread = close / (pair_close + mbt.lit(1e-12))   # mbt.lit() for constants in arithmetic
signal = -spread_z * mbt.lit(0.05)                # negation + scaling

Note: mbt.lit() is needed for constants in arithmetic (close + mbt.lit(1e-12)). Numbers auto-coerce inside mbt.when().

Sizing modes

Mode	Meaning
`FractionOfEquity` (default)	`1.0` = allocate 100% of current equity
`FractionOfInitialCapital`	`1.0` = allocate 100% of initial capital (no compounding)
`Units`	`1.0` = hold exactly 1 unit (share/contract/coin)

execution=mbt.ExecutionConfig(position_sizing_mode="FractionOfInitialCapital")

Special values

Value	Behavior
`1.0`	Full long position
`0.0`	Flat (close position)
`-0.5`	Short 50% (requires `allow_short=True`)
`NaN`	Hold current position unchanged

Parameters & Sweeps

Use mbt.param() to define sweepable parameters in indicator periods:

fast = ema(close, mbt.param("fast", default=12))
slow = ema(close, mbt.param("slow", default=50))

strategy = (
    mbt.Strategy.create("ema_cross")
    .signal("fast", fast)
    .signal("slow", slow)
    .size(mbt.when(fast > slow, 0.25, -0.25))
)

Parameters are auto-collected from expressions — no .param() needed on the Strategy.

Sweep execution

# Full sweep (returns Result per combo)
sweep = mbt.run_sweep(strategy, {"fast": [5, 12, 20], "slow": [50, 100]}, config, store)
best = sweep.best("sharpe")

# Lite sweep (metrics only, much faster for large grids)
batch = mbt.run_sweep_lite(strategy, {"fast": range(5, 100), "slow": range(10, 500)}, config, store)

Grids this size need Pro; Community is capped, and the engine tells you where you stand when you hit it. Single bt.run() calls are never gated.

run_sweep_lite is optimized for large parameter grids (100k+ combos):

Cartesian product expansion in Rust (no Python loop)
Signals and position sizing compiled as one graph, so what they share is computed once
Shared indicator cache (EMA(12) computed once, reused across combos)
Bars and higher-timeframe columns resampled once per sweep, not per combo
Metrics only — no Arrow output, and signals nothing reads are never materialised

Add device="cuda" on a machine with an NVIDIA GPU and a CUDA build. It pays off on large grids, where it is typically an order of magnitude faster; device="auto" (the default) picks between CPU and GPU for you, since the GPU loses on small ones. Results are identical either way. A strategy the GPU cannot take falls back to the CPU and says which setting caused it.

Backtest Configuration

config = mbt.BacktestConfig(
    universe=[1, 2],                       # symbol IDs
    time_range_start=start,
    time_range_end=end,
    bar_interval=Interval.hours(4),        # signal evaluation resolution
    initial_capital=10_000,
    execution=mbt.ExecutionConfig(...),
    fees=mbt.FeeConfig.binance_perps(),
    slippage=Slippage.fixed_bps(2),
    warmup_bars=60,                        # bars to skip for indicator warmup
    accuracy=False,                        # True = simulate on 1-min bars
)

Bar intervals

Interval.minutes(1)    # 1-min
Interval.minutes(15)   # 15-min
Interval.hours(1)      # 1-hour
Interval.hours(4)      # 4-hour
Interval.hours(12)     # 12-hour
Interval.days(1)       # daily

Accuracy mode

config = mbt.BacktestConfig(
    bar_interval=Interval.hours(4),   # signals on 4h
    accuracy=True,                    # simulation on 1-min bars
    ...
)

When accuracy=True, the engine loads bars_1m and runs in hybrid mode: signals evaluated on bar_interval, simulation tick-by-tick on 1-min bars. Use for precise SL/TP fill detection. ~60x slower than normal mode.

Execution Model

mbt.ExecutionConfig(
    signal_delay=0,                    # bars between signal and execution
    execution_price="AtClose",         # AtClose, AtOpen, AtVwap, MidPrice,
                                       # or ExecutionPrice.custom(name)
    max_position_pct=0.5,              # max position as fraction of equity
    allow_short=True,                  # allow short positions
    allow_fractional=True,             # allow fractional units
    position_sizing_mode="FractionOfEquity",
    pyramiding=False,                  # True = signal is delta, not target
)

Filling at a computed level

ExecutionPrice.custom(name) accepts a bar column ("vwap", ...) or the name of any signal the strategy defines, so a market fill can land on a level the DSL computes instead of the bar's close. The canonical use is a band strategy on native fine bars: the entry level is known before the bar starts, and the touch bar itself proves the level traded (it sits between open and high), yet a close fill would be systematically on the wrong side of it.

from manifoldbt.indicators import close, high, low, open
band_up, band_dn = sma * 1.012, sma * 0.992
exec_level = mbt.when(high >= band_up,
                      mbt.when(open >= band_up, open, band_up),   # gapped through
             mbt.when(low <= band_dn,
                      mbt.when(open <= band_dn, open, band_dn),
             close))
strat = strat.signal("exec_level", exec_level)
config.execution.execution_price = mbt.ExecutionPrice.custom("exec_level")

One series covers entry AND exit fills. The rules that keep it honest:

the series is read at the order's signal row, never ahead of it;
a fill outside the execution bar's [low, high] range draws a warning;
a row with no value (warm-up) falls back to the close, with a warning;
a name that is neither a column nor a signal is rejected before the run;
a bar column always wins over a same-named signal (warned about).

A custom execution price keeps the fast kernel, and the GPU. Sweeping one is as fast as sweeping a plain AtClose strategy, and device="cuda" accepts it: the level is evaluated in the kernel beside the position sizing, so correct fills no longer cost throughput. Results are identical on both devices.

Two conditions, and the sweep says so when either fails: the name must resolve to a signal rather than a bar column (a column is read per execution row, a different rule), and the strategy must have no exit orders, whose entry-bar re-check needs the general loop. run() is unaffected either way.

Signal delay

Value	Behavior
`0`	Default. Fill at the close of the signal bar
`1`	Fill on the next bar (t+1)
`2+`	Fill N bars after the signal

0 models a decision taken on the bar's own close and filled at that close, the market-on-close convention, and it is what vectorbt's from_signals does. It is the right default for coarse bars, where one bar of delay would mean pricing a full day of latency into a decision that in reality reaches the market in seconds.

Raise it when a bar is short enough that one bar is a plausible decision-to-fill latency: on 1s or sub-second bars, signal_delay=1 is the realistic setting, and 0 assumes an infinitely fast round trip. The engine does not infer this from bar_interval, so it is on you to set it.

Fee & Slippage Models

Fees

mbt.FeeConfig.binance_perps()    # maker=2bps, taker=5bps, funding
mbt.FeeConfig.binance_spot()     # maker=10bps, taker=10bps
mbt.FeeConfig.zero()             # no fees (for development)

# Custom
mbt.FeeConfig(
    maker_fee_bps=2.0,
    taker_fee_bps=5.0,
    funding_rate_column="funding_rate",
    default_fill_type="Taker",
)

Slippage

Slippage.fixed_bps(2)     # 2 bps per trade (simplest)
Slippage.volume_impact(0.1, exponent=0.5)   # qty/volume model
Slippage.spread_based(0.5)                  # spread-based

Orders (SL/TP/Trailing)

strategy = (
    mbt.Strategy.create("my_strat")
    .signal(...)
    .size(...)
    .stop_loss(pct=3.0)           # 3% stop-loss
    .take_profit(pct=5.0)         # 5% take-profit
    .trailing_stop(pct=2.0)       # 2% trailing stop
)

Entry Orders

By default an entry takes a market fill on the execution bar (see Execution Model). Four order types let the entry rest at a price instead:

Builder method	Fills when	Fill price	Costs
`.limit_entry(...)`	price comes to the level	the level exactly	maker, no slippage
`.stop_entry(...)`	price breaks through the level	the level, or the open if the bar gapped through it	taker + slippage
`.market_if_touched(...)`	price comes to the level	the level	taker + slippage
`.stop_limit_entry(...)`	breaks through `stop`, then rests at `limit`	the limit	maker, no slippage

Where the level comes from

Every method takes exactly one of three price forms:

.limit_entry(offset_bps=25)          # 25 bps below the signal close (above, for a sell)
.limit_entry(price=60_000)           # a fixed level
.limit_entry(signal="entry_px")      # a level this strategy computes

signal= is the general form: name any signal the strategy defines and the order rests on that series, read on the signal bar.

from manifoldbt.indicators import atr, close, ema

trend = ema(close, 50)
entry_px = close - atr(14)          # rest one ATR below the close

strategy = (
    mbt.Strategy.create("pullback_entry")
    .signal("trend", trend)
    .signal("entry_px", entry_px)   # named so the order can reference it
    .size(mbt.when(close > trend, 1.0, 0.0))
    .limit_entry(signal="entry_px", time_in_force={"GTB": 5})
    .stop_loss(pct=3.0)
)

Time in force

"GTC" (default, rests until filled or the signal changes), {"GTB": n} (cancel after n bars), "IOC" (fill on the arrival bar or cancel).

Three things to watch

A resting order keeps the level it was created with. signal= is read once, on the bar the order is placed, and held until the order fills, expires or is cancelled. It does not follow the series afterwards. That is the intended behaviour of a resting order, and it is the trap for a band strategy: if the band moves every bar, the order waits at a price the band has left, and a bar that gaps past the stale level still fills there. Watch the out-of-range fill warnings, which count exactly this.

If what you want is "fill wherever my level is on the bar that trades", that is not a resting order at all: use ExecutionPrice.custom, which re-reads the level every bar. Keep a resting entry for what it models, a real order sitting in the book at a price you chose.

A resting entry can simply never fill. A strategy whose entries never trigger produces a flat equity curve with no drawdown, which reads as a clean backtest. The engine counts unfilled entries and reports them:

result = mbt.run_backtest(strategy, config)
for w in result.warnings:
    print(w)   # "N entry order(s) expired unfilled and M were still resting ..."

Sizing uses the close, not the level. In FractionOfEquity mode a target of 1.0 is converted to units at the signal-bar close, so an entry resting 2% away buys ~2% too much notional. size_at_fill_price=True sizes off the order's own level instead. It is off by default because turning it on changes the results of strategies written against the old behaviour.

Cost

A conditional entry runs on the general simulation loop rather than the fast kernel, so parameter sweeps over one are slower than sweeps over a market entry and cannot use the GPU. run_sweep reports which setting took you off the fast path.

This is specific to a resting order, which can stay unfilled across bars. Filling at a computed level does not carry that cost: see Filling at a computed level, which stays on the fast kernel and on the GPU.

Cross-Asset References

Use mbt.symbol_ref() to reference another symbol's data in multi-asset strategies:

pair_close = mbt.symbol_ref("ETHUSDT", "close")
ratio = close / (pair_close + mbt.lit(1e-12))

Important: Expressions using symbol_ref() must be registered as named signals (.signal("name", expr)), not passed directly to .size(). The multi-pass evaluator needs named signals to route cross-asset data correctly.

# Required: symbol_names mapping
config = mbt.BacktestConfig(
    universe=[1, 2, 5],
    symbol_names={"BTCUSDT": 1, "ETHUSDT": 2, "BNBUSDT": 5},
    ...
)

Dataset Auto-Resolution

The engine automatically selects the best dataset based on bar_interval:

bar_interval	Dataset loaded	Bars (5 years)
1 min	`bars_1m`	~2.6M
15 min	`bars_15m`	~175k
1h - 23h	`bars_1h`	~44k
>= 24h	`bars_1d`	~1.8k

When bar_interval doesn't exactly match a dataset (e.g. 4h), the engine loads the closest smaller dataset (bars_1h) and pre-resamples to 4h before simulation.

Override with accuracy=True to always load bars_1m (precise SL/TP fills).

Override manually with dataset=:

store = mbt.DataStore(data_root="data", metadata_db="...", dataset="bars_1m")

Diagnostics

# Look-ahead bias detection
lookahead = mbt.diagnostics.detect_lookahead(strategy, config, store)
print(lookahead)  # PASS or FAIL with details

# Exposure stability (position consistency across time windows)
stability = mbt.diagnostics.check_exposure_stability(strategy, config, store)

# Post-run risk check
result = mbt.run(strategy, config, store)
risk = mbt.diagnostics.risk_check(result)

Profiling

Every result includes microsecond-precision timing:

result = mbt.run(strategy, config, store)
print(result.profile)
# {'data_load_us': 45000, 'align_us': 1000, 'signal_eval_us': 28000,
#  'runtime_prep_us': 500, 'simulation_us': 16000, 'output_build_us': 8000,
#  'total_us': 110000}

print(result.profile_summary())
# Profile (total: 110.0ms)
# ----------------------------------------
#   Data loading      45.0ms   40.9%  ################
#   Signal eval       28.0ms   25.5%  ##########
#   Simulation        16.0ms   14.5%  #####
#   ...

Complete Examples

Trend Following — EMA Crossover

import manifoldbt as mbt
from manifoldbt.indicators import close, ema
from manifoldbt.helpers import time_range, Slippage, Interval

fast = ema(close, 12)
slow = ema(close, 50)

strategy = (
    mbt.Strategy.create("trend_following")
    .signal("fast", fast)
    .signal("slow", slow)
    .size(mbt.when(fast > slow, 0.5, 0.0))
    .stop_loss(pct=3.0)
)

start, end = time_range("2022-01-01", "2025-01-01")
config = mbt.BacktestConfig(
    universe=[1], time_range_start=start, time_range_end=end,
    bar_interval=Interval.hours(12), initial_capital=10_000,
    fees=mbt.FeeConfig.binance_perps(), slippage=Slippage.fixed_bps(2),
    warmup_bars=60,
)
store = mbt.DataStore(data_root="data", metadata_db="metadata/metadata.sqlite")
result = mbt.run(strategy, config, store)
print(result.summary())

Parameter Sweep — 2D Heatmap

fast = ema(close, mbt.param("fast", default=12))
slow = ema(close, mbt.param("slow", default=50))

strategy = (
    mbt.Strategy.create("ema_cross")
    .signal("fast", fast)
    .signal("slow", slow)
    .size(mbt.when(fast > slow, 0.25, -0.25))
)

batch = mbt.run_sweep_lite(
    strategy,
    {"fast": list(range(5, 100)), "slow": list(range(10, 500))},
    config, store,
)

# Build metric grid and visualize
mbt.plot.heatmap_2d({...}, show=True)
mbt.plot.surface_3d({...}, show=True)

Statistical Arbitrage — Cross-Asset

pair_close = mbt.symbol_ref("ETHUSDT", "close")
ratio = close / (pair_close + mbt.lit(1e-12))
equilibrium = kalman(ratio, q=1e-4, r=1e-2)
spread_z = (ratio - equilibrium).zscore(28)

strategy = (
    mbt.Strategy.create("stat_arb")
    .signal("pair_close", pair_close)
    .signal("spread_z", spread_z)
    .signal("signal", -spread_z)
    .size(mbt.col("signal"))
)

config = mbt.BacktestConfig(
    universe=[1, 2, 5],
    symbol_names={"BTCUSDT": 1, "ETHUSDT": 2, "BNBUSDT": 5},
    ...
)

Indicator Reference

Moving Averages

Function	Description
`sma(source, period)`	Simple Moving Average
`ema(source, span)`	Exponential Moving Average
`dema(source, period)`	Double EMA
`tema(source, period)`	Triple EMA
`wma(source, period)`	Weighted MA
`hma(source, period)`	Hull MA
`kama(source, period)`	Kaufman Adaptive MA

Momentum

Function	Description
`rsi(source, period)`	Relative Strength Index [0-100]
`roc(source, period)`	Rate of Change
`momentum(source, period)`	Raw price difference
`macd(source, fast, slow)`	MACD line
`stoch_k(period)`	Stochastic %K
`williams_r(period)`	Williams %R
`cci(period)`	Commodity Channel Index
`adx(period)`	Average Directional Index

Volatility

Function	Description
`atr(period)`	Average True Range
`natr(period)`	Normalized ATR
`bollinger_bands(source, period, num_std)`	Returns (upper, middle, lower)
`keltner_channels(period, multiplier)`	Returns (upper, middle, lower)

Volume

Function	Description
`obv(source, vol)`	On-Balance Volume
`vwap()`	Volume-Weighted Average Price
`mfi(period)`	Money Flow Index

Filters

Function	Description
`kalman(source, q, r)`	Kalman filter
`garch(source, omega, alpha, beta)`	GARCH volatility

Statistics

Function	Description
`source.zscore(window)`	Rolling z-score
`source.linreg_slope(window)`	Linear regression slope
`source.linreg_value(window)`	Linear regression fitted value
`source.linreg_r2(window)`	Linear regression R-squared
`source.rolling_median(window)`	Rolling median

Time

Function	Description
`source.lag(n)`	Value n bars ago
`source.lead(n)`	Value n bars ahead
`source.diff(n)`	Difference over n bars
`source.pct_change(n)`	Percentage change over n bars
`source.rolling_mean(w)`	Rolling mean
`source.rolling_std(w)`	Rolling standard deviation
`source.cumsum()`	Cumulative sum

All period/window arguments accept mbt.param("name", default) for sweep grids.

Metrics Reference

Every result includes these performance metrics:

Metric	Description
`total_return`	Total return
`cagr`	Compound Annual Growth Rate
`volatility`	Annualized volatility
`sharpe`	Sharpe ratio
`sortino`	Sortino ratio
`calmar`	Calmar ratio
`max_drawdown`	Maximum drawdown
`tstat_sharpe`	t-statistic of Sharpe (sharpe * sqrt(years))
`alpha`	Annualized CAPM alpha vs buy-and-hold benchmark
`beta`	Beta to benchmark
`tstat_alpha`	t-statistic of alpha (OLS regression)

Best Practices

Set signal_delay deliberately. It defaults to 0 (fill at the signal bar's close). Raise it to 1 when one bar is a realistic decision-to-fill latency, i.e. on fine-grained bars.
Set warmup_bars to at least the longest indicator period.
Use mbt.when() for sizing. Keep signal logic readable and composable.
Run diagnostics (detect_lookahead, check_exposure_stability) on new strategies.
Start with bar_interval=hours(12) or days(1) for fast iteration, then refine with smaller intervals.
Use accuracy=True only for final validation with SL/TP — it's 60x slower.
Sweep with run_sweep_lite for large grids. Use run_sweep only when you need full Result objects.

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