mtrader 0.9.1

Vectorized backtesting & forward-testing framework for intraday strategies

mtrader

Vectorized backtesting and forward/live-testing framework for intraday trading strategies.

import mtrader as mt

result = mt.run_backtest(df, entry_conditions, buy_or_sell="buy",
                          indicators=["sma1", "rsi"], rolling_minutes=[20, 14])
result.to_html("report.html")

---

Quick start

import pandas as pd
import numpy as np
import mtrader as mt

# 1. Load & clean data
df = pd.read_csv("ticks.csv")
df = mt.clean_data(df, start_time="09:15", end_time="15:30")

# 2. Define strategy
entry = mt.cross_above("can1_sma1_p20", "close")
exit_cond = mt.cross_below("can1_rsi_p14", mt.condition("close", upper=70))

# 3. Run backtest
result = mt.run_backtest(
    df, entry, buy_or_sell="buy", exit_conditions=exit_cond,
    indicators=["sma1", "rsi"], rolling_minutes=[20, 14],
    target_delta_normalized=0.5, stoploss_delta_normalized=0.25,
)
print(result.metrics)
result.to_html("backtest.html")

---

Pipeline

raw CSV/DataFrame
    │
    ▼
clean_data()         — auto-detect types, normalize OHLCV, fill gaps, handle splits
    │
    ▼
add_indicators()     — 38+ indicators: SMA/EMA/WMA/SSMA, RSI, MACD, ATR, Stochastic,
    │                   Bollinger %B, CCI, Williams %R, ADX, MFI, PSAR, Supertrend,
    │                   Ichimoku, VWAP, Heikin Ashi, engulfing patterns, and more
    │
    ▼
precalculate_exit_time_amount_profit()
    │                 — exit signals: conditional, target, stoploss (absolute / % / column)
    │
    ▼
take_trade_on_condition*()
    │                 — capital simulation, Sharpe, drawdown
    │
    ▼
backtest_report()    — comprehensive stats: win rate, profit factor, Sharpe, Sortino, Calmar
equity_curve()       — per-bar equity + drawdown
html_backtest_report() — standalone HTML with SVG charts

---

One-call backtest

from mtrader import run_backtest

result = run_backtest(
    df,
    entry_conditions=mt.cross_above("can1_sma1_p20", "close"),
    buy_or_sell="buy",
    exit_conditions=mt.cross_below("can1_ema1_p9", "can1_ema1_p21"),
    indicators=["sma1", "ema1", "rsi"],
    rolling_minutes=[20, 9, 14],
    target_delta_normalized=0.5,
    stoploss_delta_normalized=0.25,
    initial_capital=100000,
)

result.final_capital     # 112345.67
result.metrics           # {'Sharpe Ratio': 1.23, ...}
result.report            # backtest_report dict
result.equity            # equity_curve DataFrame
result.trades            # trade_log DataFrame
result.to_html("report.html")

---

Indicators

Moving averages

Code	Description
sma	Simple Moving Average
ema	Exponential Moving Average
wma	Weighted Moving Average
ssma	Smoothed Simple Moving Average

Popular technical indicators

Code	Description
rsi	Relative Strength Index
atr	Average True Range
macd	MACD line, signal, histogram
stochk / stochd	Stochastic %K / %D
bbp	Bollinger Band %B
cci	Commodity Channel Index
willr	Williams %R
adx	Average Directional Index
mfi	Money Flow Index (volume-weighted)
obv	On-Balance Volume
psar	Parabolic SAR
supertrend	SuperTrend (returns direction 1/-1)
ichimoku	Ichimoku Cloud (tenkan/kijun/senkou A/senkou B/chikou)
ha	Heikin Ashi candles (open/high/low/close)
vwap / ewap / iwap	Volume / Equal / Incremental weighted avg price

Candlestick patterns

Code	Description
inside_bar	1 when bar inside previous bar's range
engulfing	1 for bullish engulfing, -1 for bearish engulfing

Session-aware

Code	Description
or_high / or_low	Opening range expanding high/low (first N min)
prev_day	Previous day high/low/close
pivot	Classic pivot point levels (P, S1, S2, R1, R2)
gap	Gap up/down detection

Feature codes (base signals)

Code	Signal
0, 1	close
2	av2 (H+L)/2
3	av3 (H+L+C)/3
4	av4 (O+H+L+C)/4
5	open
6	high
7	low
8..34	dif, ret, lret for 1/3/5/7/10/15/20/30/60 bars

Normalizations

Prefix an indicator name for ratio-based forms: SMN_, EMN_, WMN_, SSMN_, SVN_, EVN_, WVN_, SSVN_, Z_, BRN_, TMN_.

Suffix controls denominator: ""/F = signal itself, P/0 = close, B = base signal, numeric code = that feature.

Column naming

Indicators computed via add_indicators() produce columns named can1_{indicator}_{period}:

• can1_sma1_p20 — SMA(20) on close
• can1_rsi_p14 — RSI(14)
• can1_macd — MACD line (no period suffix, fixed 12/26/9)
• can1_obv — On-Balance Volume

---

Conditions

Conditions use a declarative dict format:

{
    "first_column_name": "can1_sma1_p20",
    "second_column_name": "close",
    "shift_down_first": 0,
    "shift_down_second": 0,
    "lower_range_of_difference": 0,      # first - second >= lower
    "upper_range_of_difference": np.inf, # first - second <= upper
    "perform_normalization_of_diff": False,
}

Helpers build these for you:

from mtrader import condition, cross_above, cross_below

cond = condition("close", "can1_sma1_p20", lower=0)           # close > sma
crossover = cross_above("can1_macd", "can1_macdsignal")       # MACD crossover
crossunder = cross_below("can1_stochk_p14", "can1_stochd_p14") # Stochastic crossunder

AND within a group, OR across groups:

[
    {"first": "ema9", "second": "ema21", ...},  # Group 1: both must be true
    {"first": "close", "second": "vwap", ...},
]
# OR
[
    [{"first": "rsi", "second": "30", ...}],     # Group 1 OR Group 2
    [{"first": "stochk", "second": "20", ...}],
]

---

Exit strategy

from mtrader import precalculate_exit_time_amount_profit

df = precalculate_exit_time_amount_profit(
    df, exit_conditions, buy_or_sell="buy",
    target_delta=200,                # absolute price target
    stoploss_delta=100,              # absolute stoploss
    target_delta_normalized=0.5,     # 0.5% target
    stoploss_delta_normalized=0.25,  # 0.25% stoploss
    target_delta_column="can1_atr_p14",      # per-bar column values
    stoploss_delta_column="can1_atr_p14_halfloss",
)

---

Trade simulation

trades, final_capital, metrics = mt.take_trade_on_condition_numpy(
    df, entry_conditions, leverage=1, initial_capital=100000)
# metrics: Sharpe Ratio, Volatility, Max Drawdown

CuPy-accelerated variants: take_trade_on_condition, take_trade_on_condition2, take_trade_on_condition3.

Grid search variants: take_trade_on_condition_vectorized, take_trade_on_condition_vectorized2.

---

Performance reports

from mtrader import backtest_report, equity_curve, html_backtest_report

report = backtest_report(df, initial_capital=1000)
# total_trades, win_rate_pct, profit_factor, avg_win/loss_pct,
# sharpe_ratio, sortino_ratio, calmar_ratio, max_drawdown_pct,
# max_consecutive_wins/losses, ...

eq = equity_curve(df)
# columns: datetime, equity, drawdown_pct, trade

html_backtest_report(result, output_path="report.html")
# standalone HTML with SVG charts, no external dependencies

---

Exit optimization

from mtrader import find_best_exit

best, results = find_best_exit(
    df, entry_conditions, buy_or_sell="buy",
    target_deltas=[50, 100, 150, 200],
    stoploss_deltas=[25, 50, 75, 100],
    metric="sharpe",      # or "final_capital", "max_drawdown"
    verbose=True,
)
print(best)  # {'target_delta': 150, 'stoploss_delta': 75}

---

Strategy object (serializable)

from mtrader import Strategy

strat = Strategy(
    name="SMA Crossover",
    indicators=["sma1", "ema1"],
    rolling_minutes=[20, 50],
    entry_conditions=cross_above("can1_sma1_p20", "can1_sma1_p50"),
    exit_conditions=cross_below("can1_sma1_p20", "can1_sma1_p50"),
    side="buy",
)

result = strat.run(df)
strat.save("strat.json")
loaded = Strategy.load("strat.json")

live_engine = strat.to_live(df)  # convert to live engine with warmup

---

Walk-forward optimization

from mtrader import walk_forward_optimize, Strategy

results = walk_forward_optimize(
    df,
    strategy_factory=lambda params: Strategy(**params),
    param_grid={"rolling_minutes": [[10, 30], [20, 50]]},
    train_days=60,
    test_days=20,
    metric="sharpe",
)

---

Position sizing & risk controls

from mtrader import (
    fixed_quantity_size, fixed_capital_size,
    percent_equity_size, atr_risk_size,
    apply_risk_controls, india_intraday_cost_model,
)

qty = atr_risk_size(price, atr_values, equity=100000, risk_pct=0.01)
trades = apply_risk_controls(trades, max_trades_per_day=3, cooldown_bars=5)

---

Multi-symbol portfolio

from mtrader import run_portfolio

data = {"AAPL": df_aapl, "TSLA": df_tsla, "GOOG": df_goog}
result = run_portfolio(data, strategy, initial_capital=300000)

---

Live trading

from mtrader import live_strategy_from_history, stream_live_signals

engine = live_strategy_from_history(
    df, indicators=["sma1", "ema1", "rsi"],
    periods=[20, 50, 14],
    entry_conditions=cross_above("can1_sma1_p20", "can1_sma1_p50"),
    side="buy",
)

for signal in stream_live_signals(engine, candle_feed):
    if signal["action"] == "BUY":
        place_order(signal)

Incremental indicators (EMA, RSI, ATR, VWAP) update in O(1) per bar.

---

Data cleaning

from mtrader import clean_data

cleaned = clean_data(
    raw_df,
    start_time="09:15", end_time="15:30",
    start_date="2024-01-01", end_date="2024-12-31",
    fill_gap=True, adjustsplit=True, multiplier=100,
)

Auto-detects: datetime columns, OHLCV columns, stock splits/reverse mergers, gap-fills to 1-min frequency.

---

Modules

Module	Key exports
data_cleaner	clean_data, detect_data_types_with_formats, fill_missing_rows
indicators	All standalone indicator functions (ema, rsi, macd, psar, ichimoku, ...)
indicator_engine	add_indicators, add_indicators_on_group, FEATURE_CODE
exit_strategy	precalculate_exit_time_amount_profit
trading	take_trade_on_condition*, update_cond
optimize_exit	find_best_exit
backtest	run_backtest, BacktestResult, condition, cross_above/below, walk_forward_splits, parameter_grid, trade_log
advanced	Strategy, CostModel, sizing functions, run_portfolio, walk_forward_optimize, random_parameter_search, resample_ohlcv
live	LiveIndicatorEngine, LiveStrategyEngine, stream_live_signals, live_strategy_from_history
report	backtest_report, equity_curve, html_backtest_report
monotonic_stack	monotonic_stack_for_value1_gt/lt_value2
utils	timenum

---

Testing

python -m pytest src/tests/ -v

46 tests cover: data cleaning, all indicators, exit precalculation, trade simulation (NumPy + CuPy), exit optimization, performance reports, and 20 strategy scenarios.

---

Dependencies

• Required: numpy, pandas, inspecty
• Optional: numba (monotonic stack), cupy (GPU trading)

---

License

MIT