AlgoTradeKit 0.6.0

Algorithmic Trading Toolkit — data collection, indicators, strategy, simulation, and live trading

AlgoTradeKit

Algorithmic Trading Toolkit — collect market data, build indicators and strategies, backtest with a full trading simulation, and trade live.

pip install AlgoTradeKit

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Modules

Module	Status	Description
data	✅ v0.1.0	Collect OHLCV candles from exchanges
indicator	✅ v0.4.0	RSI, EMA/SMA/WMA/VWAP, MACD, Ichimoku (no TA-lib)
strategy	✅ v0.5.0	Build and combine trading strategies
simulate	✅ v0.6.0	Backtest strategies with realistic cost and TP/SL models
visual	✅ v0.3.0	Interactive candlestick charts via FastAPI + WebSocket
trade	🔜 planned	Live trading via exchange API or MT5

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Quick Start

1 · Collect candle data

from AlgoTradeKit.data import Collector

collector = Collector(source="binance-futures", symbol="BTCUSDT", timeframe="1h")
collector.destination = "data/"
collector.starttime   = "2022/01/01"
collector.collect()

2 · Compute indicators

import pandas as pd
from AlgoTradeKit.indicator import EMA, RSI, MACD, Ichimoku

df  = pd.read_csv("data/binance-futures_BTCUSDT_1h.csv")
ema = EMA(length=50).calculate(df)
rsi = RSI(length=14).calculate(df)

3 · Build a strategy

from AlgoTradeKit.strategy import BaseStrategy, Signal

class MyStrategy(BaseStrategy):
    primary_timeframe = "1h"
    warmup_period     = 50

    def prepare_indicators(self, data):
        data["1h"]["ema50"] = EMA(50).calculate(data["1h"])["ema50"]
        return data

    def generate_signals(self, candle_index, data):
        df  = data["1h"]
        row = df.iloc[candle_index]
        if row["close"] > row["ema50"]:
            return [Signal(
                direction="long",
                entry_price=row["close"],
                stop_loss=row["close"] * 0.98,
                take_profit=None,
                timestamp=int(row["timestamp"]),
                candle_index=candle_index,
                timeframe="1h",
            )]
        return []

4 · Simulate (backtest)

from AlgoTradeKit.simulate import Simulate, SimulateConfig

config = SimulateConfig(
    initial_balance=10_000,
    symbol="btcusdt",
    exchange_type="exchange",   # or "metatrader"
    leverage=10,
    spread=0.5,                 # $0.50 bid-ask spread
    commission_type="percentage",
    commission=0.001,           # 0.1 % per side
    position_sizing="risk_percent",
    risk_per_trade=1.0,         # risk 1 % of balance per trade
    tp_mode="multi_rr",         # trail SL after each TP level
    tp_levels=[1.0, 2.0, 3.0], # close at 1R, 2R, 3R
    sl_mode="signal",
    risk_free_enabled=True,     # break-even after 1R profit
    max_positions=3,
    primary_timeframe="1h",
)

strategy = MyStrategy()
data     = {"1h": df}

result = strategy.run(data)
report = Simulate(config).run(result)

print(report)
# → <SimulateReport 'BTCUSDT_risk1.0pct_lev10x_tp_multi_rr_[1.0_2.0_3.0]'
#       trades=47 pnl=+1243.80 (+12.4%) wr=61.7%>

print(f"Profit factor : {report.profit_factor:.2f}")
print(f"Max drawdown  : {report.max_drawdown.drawdown_percent:.1f}%")
print(f"Sharpe ratio  : {report.sharpe_ratio:.2f}")

5 · Sweep configs in parallel

from AlgoTradeKit.simulate import run_batch

configs = [
    SimulateConfig(risk_per_trade=0.5, tp_mode="fixed_rr", tp_rr=1.5),
    SimulateConfig(risk_per_trade=1.0, tp_mode="fixed_rr", tp_rr=2.0),
    SimulateConfig(risk_per_trade=2.0, tp_mode="fixed_rr", tp_rr=3.0),
]
reports = run_batch(strategy, data, configs)
best    = max(reports, key=lambda r: r.total_pnl)
print(f"Best: {best.config.config_id}  PnL={best.total_pnl:.2f}")

6 · Multi-symbol portfolio (shared wallet)

from AlgoTradeKit.simulate import run_multi

report = run_multi([
    (btc_strategy, btc_data, SimulateConfig(symbol="btcusdt", risk_per_trade=1.0)),
    (eth_strategy, eth_data, SimulateConfig(symbol="ethusdt", risk_per_trade=0.5)),
])
print(f"Portfolio PnL: {report.total_pnl:.2f}")

7 · Visualise

from AlgoTradeKit.visual import Chart

chart = Chart.from_csv("data/binance-futures_BTCUSDT_1h.csv")
chart.serve()   # opens browser at localhost:8000

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SimulateConfig reference

Parameter	Default	Description
initial_balance	10_000	Starting wallet balance
symbol	""	Instrument (required for MT5 lot sizing)
exchange_type	"exchange"	"exchange" or "metatrader"
leverage	1.0	Leverage multiplier
spread	0.0	Bid-ask spread in price units
commission_type	"percentage"	"percentage" / "per_lot" / "fixed"
commission	0.001	Commission rate or amount
position_sizing	"risk_percent"	"risk_percent" / "fixed_amount" / "fixed_lot"
risk_per_trade	1.0	% of balance to risk
compound	False	Size off current balance (True) or initial (False)
max_positions	1	Max simultaneous positions
max_long_positions	1	Max simultaneous longs
max_short_positions	1	Max simultaneous shorts
tp_mode	"signal"	"signal" / "fixed_rr" / "multi_rr" / "none"
tp_rr	2.0	Risk-reward ratio for "fixed_rr"
tp_levels	[1,2,3]	R-multiples for "multi_rr"
sl_mode	"signal"	"signal" / "trailing"
trailing_sl_percent	1.0	Trailing SL % from peak price
risk_free_enabled	False	Move SL to entry at N×R profit
risk_free_at_rr	1.0	R-multiple that activates break-even
force_close_on_exit_signal	False	Close on strategy ExitSignal
drawdown_threshold	5.0	Report drawdowns ≥ this %
primary_timeframe	"1h"	TF key in StrategyResult.data

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Installation

Requires Python 3.10+

pip install AlgoTradeKit

For development:

git clone https://github.com/AmirMohammadBazdar/AlgoTradeKit.git
cd AlgoTradeKit
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

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Requirements

• pandas >= 2.0
• requests >= 2.28

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Roadmap

• trade module — live trading via Binance API / MT5
• report module — HTML/PDF report with interactive balance chart, drawdown overlay, session heatmaps
• MEXC, Bybit, OKX data sources
• WebSocket live data bridge

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License

MIT — see LICENSE