AlgoTradeKit 0.9.0

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

AlgoTradeKit

AlgoTradeKit is a modular Python library for building, backtesting, and visualising algorithmic trading strategies. Every indicator is implemented from scratch — no pandas-ta, no ta-lib — so you have full control over every calculation.

pip install AlgoTradeKit

Requires Python 3.10+

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Table of Contents

1. Architecture Overview
2. broker — Exchanges & MetaTrader
3. data — OHLCV Collection
4. indicator — Technical Indicators
5. strategy — Signal Generation
6. simulate — Backtesting Engine
7. visual — Interactive Chart
8. report — Simulation Report
9. Built-in MACD Strategy Demo
10. Configuration Reference

---

Architecture Overview

AlgoTradeKit/
├── broker/         Unified exchange & MetaTrader access — candles, orders, account, sockets  ← new in v0.9.0
├── data/           Download and cache OHLCV candles (now via the broker module)
├── indicator/      RSI, MACD, EMA, SMA, Bollinger Bands, ATR, Ichimoku
├── strategy/       BaseStrategy, Signal, StrategyResult, built-in strategies
├── simulate/       Backtesting engine, position management, SimulateReport
├── visual/         Interactive candlestick chart served in your browser
└── report/         Interactive simulation report web page  ← new in v0.7.0

Data flows in one direction:

broker ──► data ──► indicator ──► strategy ──► simulate ──► report
                                                   └──────► visual

---

broker — Exchanges & MetaTrader (v0.9.0)

One unified door to every venue. Create a connection with Broker(...) and hand the result to data.Collector (candles / streaming) or use it directly for account info and orders. A Binance spot account, a Binance USD-M futures account, and a MetaTrader forex account all expose the same BaseBroker interface — the connector hides each venue's quirks.

Market data — no credentials needed:

from AlgoTradeKit.broker import Broker

b = Broker("binance-futures")                       # public: no API keys
candles = b.fetch_last_candles("BTCUSDT", "1h", 500)  # list of standard candle dicts
tick    = b.get_ticker("BTCUSDT")                     # last / bid / ask

Trading — credentials required (testnet=True for the sandbox):

b = Broker("binance-futures", api_key="…", api_secret="…", testnet=True)

b.set_leverage("BTCUSDT", 10)
res = b.create_market_order("BTCUSDT", "buy", 0.001,
                            stop_loss=58000, take_profit=72000)  # SL/TP = reduce-only orders
print(b.open_positions(), b.get_account_info().equity)
b.close_position("BTCUSDT")

Order placement is safe-by-default in the sense that private calls need explicit credentials; live endpoints are the default and testnet=True opts into the sandbox.

Real-time sockets (both Binance spot & futures):

stream = b.stream_candles("BTCUSDT", "1m", lambda c: print(c["close"]), closed_only=True)
# … later …
stream.stop()

MetaTrader (forex) on a headless Linux VPS

MetaTrader has no public API — the terminal speaks a proprietary protocol to your broker's server. To run head­less (SSH-only, no GUI) you install MT5 + Windows Python under Wine and run the bundled bridge server under xvfb. AlgoTradeKit's Linux side then reaches MT5 only through that bridge, so MetaTrader5 is never a dependency of the library itself (no conflict).

# On the VPS, inside the Wine Python (headless):
wine python -m pip install MetaTrader5
xvfb-run wine python bridge_server.py --host 127.0.0.1 --port 18812 \
    --login 12345678 --password "***" --server "MyBroker-Demo"

# On the normal Linux side:
mt = Broker("metatrader", server="MyBroker-Demo", login=12345678, password="***")
candles = mt.fetch_last_candles("EURUSD", "15m", 1000)
mt.create_market_order("EURUSD", "buy", 0.10, stop_loss=1.0800, take_profit=1.1000)

bridge_server.py ships in AlgoTradeKit/broker/metatrader/ — see its header for the full Wine setup.

---

data — OHLCV Collection

The data module now sits on top of broker. Collector takes either a venue name or a ready Broker instance:

from AlgoTradeKit.data import Collector
from AlgoTradeKit.broker import Broker

# way 1 — by name (Collector builds the connector)
c = Collector("binance-futures", "BTCUSDT", "1h")

# way 2 — bring your own Broker (e.g. authenticated, or a MetaTrader forex link)
c = Collector(Broker("metatrader", server="MyBroker-Demo", login=1, password="…"),
              "EURUSD", "15m")

# real-time candles for either venue
stream = c.stream(lambda candle: print(candle["close"]))

from AlgoTradeKit.data import Collector

collector = Collector(
    exchange="binance_futures",
    symbol="BTCUSDT",
    timeframes=["1h", "4h"],
    save_dir="data/",
)
data = collector.fetch(start="2024-01-01", end="2024-12-31")
# data["1h"] → pd.DataFrame  columns: timestamp(UTC ms), open, high, low, close, volume

Load from an existing CSV:

import pandas as pd
data = {"1h": pd.read_csv("data/binance-futures_BTCUSDT_1h.csv")}

Normalizing non-standard CSVs (v0.7.2)

Broker / MT5 exports often use Unix-second timestamps and omit optional columns. Normalizer converts any OHLCV CSV to the library standard automatically:

from AlgoTradeKit.data import Normalizer

# Accepts: timestamp in seconds OR milliseconds (auto-detected)
# Required columns: timestamp, open, high, low, close
# Optional: volume (defaults to 0 if absent)
norm = Normalizer("USDJPY_1m.csv")
norm.start = "2022/01/01"   # optional date filter
norm.end   = "2024/01/01"

# Return as DataFrame (no file written)
df = norm.normalize()

# Save as library-standard CSV
path = norm.save(destination="data/")

# One-liner: normalize + save
df, path = Normalizer("USDJPY_1m.csv").normalize_and_save(destination="data/")

---

indicator — Technical Indicators

All indicators are built from scratch — no third-party TA wrappers.

from AlgoTradeKit.indicator import ATR, RSI, MACD, EMA, SMA, Ichimoku

close = df["close"]   # pd.Series

rsi  = RSI(close, length=14)           # rsi.rsi
macd = MACD(close, fast=12, slow=26, signal=9)
                                        # macd.macd, .signal, .histogram
ema  = EMA(close, length=20)           # ema.ema
sma  = SMA(close, length=50)           # sma.sma
atr  = ATR(df["high"], df["low"], close, period=14)  # atr.atr, atr.tr
ichi = Ichimoku(df["high"], df["low"], close)
                                        # ichi.tenkan / kijun / senkou_a / senkou_b
                                        # ichi.chikou / cloud_future_a / cloud_future_b
                                        # ichi.span_a_raw / span_b_raw  ← v0.7.2

ATR — Average True Range (v0.7.2)

Wilder's smoothing (RMA, alpha = 1/period). Matches TradingView ta.atr().

atr = ATR(df["high"], df["low"], df["close"], period=14)
df["atr"] = atr.atr.values   # Wilder-smoothed ATR
df["tr"]  = atr.tr.values    # raw True Range

Ichimoku.span_a_raw / .span_b_raw (v0.7.2)

Unshifted Senkou Span A and B (at the current bar, before the displacement shift is applied). Useful in strategy logic where you need the live cloud value:

ichi = Ichimoku(high, low, close, displacement=26)
# ichi.senkou_a   = span_a_raw.shift(26)  — displayed cloud (forward-shifted)
# ichi.span_a_raw = (tenkan + kijun) / 2  — current bar value (unshifted)

---

strategy — Signal Generation

Subclass BaseStrategy, implement two methods, and return Signal objects.

from AlgoTradeKit.strategy import BaseStrategy, Signal, StrategyMode

class EMAStrategy(BaseStrategy):

    def prepare_indicators(self, data):
        df = data["1h"].copy()
        df["ema20"] = EMA(df["close"], 20).value
        return {**data, "1h": df}

    def generate_signals(self, data):
        df, signals = data["1h"], []
        for i in range(1, len(df)):
            r, p = df.iloc[i], df.iloc[i - 1]
            if p["close"] < p["ema20"] and r["close"] >= r["ema20"]:
                signals.append(Signal(
                    direction="long",
                    entry_price=r["close"],
                    stop_loss=r["close"] * 0.98,
                    take_profit=r["close"] * 1.04,
                    timestamp=int(r["timestamp"]),
                    candle_index=i,
                    timeframe="1h",
                ))
        return signals

result = EMAStrategy().run(data, mode=StrategyMode.BACKTEST)

Strategy Drawings (v0.7.0)

Strategies can attach visual drawings so they appear on the candle chart:

# In generate_signals or prepare_indicators:
self._drawings.append({
    "type": "hline", "price": 42000.0,
    "color": "#58a6ff", "label": "Support",
})

# Return them in StrategyResult:
return StrategyResult(..., drawings=self._drawings)

Built-in Strategies

Strategy	Import
MACD Crossover	from AlgoTradeKit.strategy.builtin.macd import MACDCrossoverStrategy

from AlgoTradeKit.strategy.builtin.macd import MACDCrossoverStrategy

strategy = MACDCrossoverStrategy(fast=12, slow=26, signal=9,
                                  sl_atr_multiplier=1.5, timeframe="1h")
result = strategy.run(data)

---

simulate — Backtesting Engine

Replay StrategyResult signals candle-by-candle and produce a SimulateReport.

Basic Usage

from AlgoTradeKit.simulate import Simulate, SimulateConfig

config = SimulateConfig(
    initial_balance=10_000,
    symbol="btcusdt",
    leverage=10,
    commission=0.001,         # 0.1% per side
    risk_per_trade=1.0,       # 1% of balance at risk
    tp_mode="fixed_rr",
    tp_rr=2.0,
    primary_timeframe="1h",
)
report = Simulate(config).run(strategy_result)
print(report)

Auto Visualisation (v0.7.0)

config = SimulateConfig(
    ...
    show_chart=True,          # open candle chart with position boxes
    report_mode="both",       # "none" | "webpage" | "save" | "both"
    report_save_path="report.html",
)
report = Simulate(config).run(strategy_result)
# → two browser tabs open: candle chart and report page
# → report.html saved to disk

SimulateConfig Key Fields

Field	Default	Description
initial_balance	10 000	Starting wallet balance
leverage	1.0	Leverage multiplier
commission	0.001	0.1% per side (percentage mode)
risk_per_trade	1.0	% of balance at risk per trade
tp_mode	"signal"	"signal"/"fixed_rr"/"multi_rr"/"none"
tp_rr	2.0	R:R ratio for fixed_rr mode
tp_levels	[1,2,3]	R levels for multi_rr mode
tp_level_close_fractions	None	v0.7.3 Fraction of original size to realise at each tp_levels entry (multi_rr only) — see below
sl_mode	"signal"	"signal" or "trailing"
risk_free_enabled	False	Move SL to break-even at risk_free_at_rr
show_chart	False	v0.7.0 Open candle chart after run
report_mode	"none"	v0.7.0 Post-run report rendering
report_save_path	"report.html"	v0.7.0 HTML save path
chart_indicators	[]	v0.8.0 Indicator specs drawn on the show_chart chart (backend-computed) — see below

Per-Signal Sizing & Partial Take-Profit (v0.7.3)

Signal.risk_multiplier scales one signal's size relative to the run's sizing config — useful for varying risk by context, or for splitting one trade idea into several sub-positions whose sizes sum to one risk unit:

# Three signals at the same candle, each risking 1/3 of a normal trade,
# targeting 1R / 2R / 3R — equivalent to scaling out of one position.
for i, tp_r in enumerate([1, 2, 3], start=1):
    signals.append(Signal(
        direction="long", entry_price=entry, stop_loss=sl,
        take_profit=entry + tp_r * (entry - sl),
        timestamp=ts, candle_index=idx, timeframe="1h",
        risk_multiplier=1 / 3,
    ))

SimulateConfig.tp_level_close_fractions turns multi_rr into a true scale-out — each level realises a real, partial close instead of only moving the SL:

config = SimulateConfig(
    ...,
    tp_mode="multi_rr",
    tp_levels=[1.0, 2.0, 3.0],
    tp_level_close_fractions=[1 / 3, 1 / 3, 1 / 3],  # bank 1/3 at each level
)

Set every fraction to 0.0 instead to get the opposite pattern — SL walks through every level but nothing closes until the position is eventually stopped out, letting winners run with a trailing stop and no fixed target.

Batch Sweep

from AlgoTradeKit.simulate import run_batch

reports = run_batch(strategy, data, [
    SimulateConfig(tp_rr=1.5),
    SimulateConfig(tp_rr=2.0),
    SimulateConfig(tp_rr=3.0),
])
best = max(reports, key=lambda r: r.sharpe_ratio)

Multi-Pair Portfolio

from AlgoTradeKit.simulate import run_multi

report = run_multi([
    (btc_strategy, btc_data, SimulateConfig(symbol="btcusdt")),
    (eth_strategy, eth_data, SimulateConfig(symbol="ethusdt")),
], initial_balance=10_000)

SimulateReport

report.total_pnl          # float
report.win_rate           # float (%)
report.profit_factor      # gross profit / gross loss
report.sharpe_ratio       # annualised Sharpe
report.sortino_ratio
report.calmar_ratio
report.max_drawdown       # DrawdownPeriod
report.significant_drawdowns  # list[DrawdownPeriod]
report.weekday_stats      # dict[str, WeekdayStats]
report.session_stats      # dict[str, SessionStats]  (London/NY/Tokyo/Sydney)
report.monthly_stats      # dict[str, MonthStats]
report.balance_history    # list[dict]
report.trade_markers      # list[dict]

---

visual — Interactive Chart

Interactive candlestick chart served locally, based on TradingView's lightweight-charts.

from AlgoTradeKit.visual import Chart

chart = Chart.from_csv("data/binance-futures_BTCUSDT_1h.csv")
chart.show(block=True)

Candle range filter (v0.7.2)

Restrict which candles are visible without modifying the source data:

# Show a specific datetime range
chart = Chart.from_csv("data/btc_1h.csv",
    candle_range={"start": "2024/01/01", "end": "2024/06/01"})

# From a date to the last candle
chart = Chart.from_csv("data/btc_1h.csv",
    candle_range={"start": "2024/06/01"})

# From the first candle to a date
chart = Chart.from_csv("data/btc_1h.csv",
    candle_range={"end": "2024/01/01"})

# Last / first N candles
chart = Chart.from_csv("data/btc_1h.csv", candle_range={"last_n": 500})
chart = Chart.from_csv("data/btc_1h.csv", candle_range={"first_n": 200})

# Also available on set_data()
chart.set_data(df, candle_range={"start": "2023/01/01", "end": "2024/01/01"})

Add Indicators

from AlgoTradeKit.visual import add_rsi, add_macd, add_ma, add_ichimoku
from AlgoTradeKit.indicator import RSI, MACD, EMA, Ichimoku

add_rsi(chart,  RSI(df["close"]),             timestamps=df["timestamp"])
add_macd(chart, MACD(df["close"]),            timestamps=df["timestamp"])
add_ma(chart,   EMA(df["close"], 20),         timestamps=df["timestamp"], name="EMA 20")
add_ichimoku(chart, Ichimoku(df["high"], df["low"], df["close"]),
                              timestamps=df["timestamp"])

Drawings

chart.add_hline(price=42_000, label="Support")
chart.add_box(time1=1700000000, price1=41_000,
              time2=1700007200, price2=43_000, opacity=0.1)
chart.add_signal(time=1700000000, side="buy")

Position Boxes (v0.7.0)

TradingView-style position boxes showing SL/TP zones with R:R labels:

from AlgoTradeKit.visual.indicator_renderer import add_simulation_positions

chart = Chart.from_csv("data/binance-futures_BTCUSDT_1h.csv")
add_simulation_positions(chart, report, opacity=0.15, config=sim_config)
chart.show(block=True)

Dynamic SL/TP lines (v0.7.4) — when config is passed, coloured horizontal lines are drawn on the chart for every trade where the SL moved:

Colour	Meaning
🔴 Red	SL is in the loss zone (below entry for long)
🟡 Amber	SL is at break-even (entry price)
🔵 Cyan	SL is in profit territory
🟢 Green	Next TP target (multi_rr only)

The position box's profit-zone boundary is also corrected to show the TP target that was active at the moment of closing rather than the initial TP at entry. For trailing-SL trades the box top is set to peak_price (the highest price the trailing SL ever chased).

Manual single box:

chart.add_position_box(
    open_time=1700000000, close_time=1700003600,
    entry_price=50_000, stop_loss=49_500, take_profit=51_000,
    direction="long", net_pnl=100.0, close_reason="tp",
    trade_id=1, rr_ratio=2.0,
)

Indicator Toolbar (v0.8.0)

The chart toolbar has an INDICATORS button on the left. Clicking it opens a panel where indicators can be added — and edited — interactively at runtime, no code required:

• Moving averages: EMA, SMA, WMA, SMMA, DEMA, TEMA, HMA, VWMA, VWAP
• Oscillators: RSI (with optional MA), MACD, ATR
• Trend: Ichimoku Cloud (Tenkan / Kijun / Senkou B / Displacement)

Each type shows its own parameter form. After clicking Add to Chart, the backend computes the indicator from the OHLCV data and the chart updates immediately (no maths in the browser). Every indicator on the chart gets a ⚙ gear icon in the legend — click it to re-open the panel pre-filled with the current settings; saving recomputes it server-side.

Indicators on the Simulation Chart (v0.8.0)

When show_chart=True, pass chart_indicators to draw indicators on the simulation chart automatically. Use the same parameters your strategy trades on to mirror it exactly, add extra indicators, or both — all computed in the backend before the chart opens (and still editable live via the toolbar):

config = SimulateConfig(
    show_chart=True,
    chart_indicators=[
        {"kind": "ichimoku", "tenkan": 8, "kijun": 22,
         "senkou_b": 44, "displacement": 22},   # match the strategy
        {"kind": "rsi", "period": 14, "source": "close"},
        {"kind": "ema", "period": 200, "source": "close"},  # extra
    ],
)

Strategy Drawings (v0.7.0)

from AlgoTradeKit.visual.indicator_renderer import add_strategy_drawings
add_strategy_drawings(chart, strategy_result)

Navigate to Candle (v0.7.0)

chart.navigate_to_candle(timestamp_ms=1700000000000)

---

report — Simulation Report (v0.7.0)

An interactive single-page web report for a SimulateReport.

Show in Browser

from AlgoTradeKit.report import show_report
show_report(report, block=True)

Save as Standalone HTML

from AlgoTradeKit.report import save_report_html
save_report_html(report, "report.html")

Report Page Sections

Section	Contents
Header	Symbol, config ID, PnL badge, PDF export
Config	All SimulateConfig parameters
Equity curve	Wallet/equity line, max DD shading, DD regions, trade dots (zoom/pan)
Trade tooltip	Entry/exit, SL/TP, PnL, R-multiple, close reason + "Open Chart" button
Performance KPIs	PnL%, win rate, avg win/loss, largest win/loss, avg R
Risk metrics	Profit factor, expectancy, Sharpe, Sortino, Calmar, recovery factor
Trade stats	Total/long/short, win/loss/BE, SL/TP/RF/FC/EOD counts
Drawdown table	All DDs above threshold, sorted by severity
Weekday analysis	Trades, win%, PnL per weekday
Session analysis	London, New York, Tokyo, Sydney, Off-Hours
Monthly analysis	Per-month: trades, win%, total PnL, avg PnL
Cost summary	Commission, spread, avg MAE, avg MFE

Clicking a trade dot and pressing "Open on Candle Chart" navigates the linked chart to that trade's entry candle.

---

Built-in MACD Strategy Demo

"""demo_macd.py — run MACD strategy, open chart + report."""

import pandas as pd
from AlgoTradeKit.strategy.builtin.macd import MACDCrossoverStrategy
from AlgoTradeKit.simulate import Simulate, SimulateConfig

# Load data (replace path with your CSV)
data = {"1h": pd.read_csv("data/binance-futures_BTCUSDT_1h.csv")}

# Run strategy
strategy = MACDCrossoverStrategy(fast=12, slow=26, signal=9,
                                  sl_atr_multiplier=1.5, timeframe="1h")
result = strategy.run(data)
print(f"Signals: {result.signal_count}")

# Simulate with auto chart + report
config = SimulateConfig(
    symbol="btcusdt",
    leverage=10,
    commission=0.001,
    risk_per_trade=1.0,
    tp_mode="fixed_rr",
    tp_rr=2.0,
    show_chart=True,
    report_mode="webpage",
)
report = Simulate(config).run(result)
print(report)

# Keep servers alive
import time
try:
    while True: time.sleep(1)
except KeyboardInterrupt:
    pass

Run:

python demo_macd.py

Two browser tabs open:

• Candle chart — with MACD indicator and position boxes for every trade
• Report page — equity curve, full metrics, drawdown table, time analysis

---

Configuration Reference

Report Mode Constants

Constant	Value	Behaviour
REPORT_MODE_NONE	"none"	No report (default)
REPORT_MODE_WEBPAGE	"webpage"	Open in browser
REPORT_MODE_SAVE	"save"	Save standalone HTML
REPORT_MODE_BOTH	"both"	Open + save

TP Mode Constants

Constant	Value	Behaviour
TP_MODE_SIGNAL	"signal"	Use Signal's take_profit
TP_MODE_FIXED_RR	"fixed_rr"	entry ± tp_rr × SL distance
TP_MODE_MULTI_RR	"multi_rr"	Multiple levels, SL trails (and optionally partially closes — see tp_level_close_fractions (v0.7.3))
TP_MODE_NONE	"none"	No TP

Close Reason Constants

Constant	Value	Meaning
CLOSE_REASON_SL	"sl"	Stop loss hit
CLOSE_REASON_TP	"tp"	Take profit hit — nothing left open afterwards
CLOSE_REASON_TP_PARTIAL	"tp_rr"	v0.7.3 Intermediate multi_rr level partially realised — position still open with reduced size
CLOSE_REASON_RF	"rf"	Risk-free / trailed SL hit
CLOSE_REASON_FC	"force_close"	Closed by an ExitSignal
CLOSE_REASON_EOD	"end_of_data"	Still open when data ran out

SL Mode Constants

Constant	Value	Behaviour
SL_MODE_SIGNAL	"signal"	Use Signal's stop_loss
SL_MODE_TRAILING	"trailing"	Trail trailing_sl_percent% from peak

---

License

MIT — see LICENSE.