Metadata-Version: 2.4
Name: AlgoTradeKit
Version: 0.4.1
Summary: Algorithmic Trading Toolkit — data collection, indicators, strategy, simulation, and live trading
Project-URL: Homepage, https://github.com/AmirMohammadBazdar/AlgoTradeKit
Project-URL: Repository, https://github.com/AmirMohammadBazdar/AlgoTradeKit
Project-URL: Issues, https://github.com/AmirMohammadBazdar/AlgoTradeKit/issues
Project-URL: Changelog, https://github.com/AmirMohammadBazdar/AlgoTradeKit/blob/main/CHANGELOG.md
Author-email: Amir Mohammad Bazdar <ambazdar03@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Amir Mohammad Bazdar | امیر محمد بازدار
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: algorithmic-trading,backtesting,binance,crypto,finance,indicators,quant,trading-bot
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.10
Requires-Dist: fastapi>=0.110.0
Requires-Dist: pandas>=2.0
Requires-Dist: requests>=2.28
Requires-Dist: uvicorn[standard]>=0.29.0
Requires-Dist: websockets>=12.0
Provides-Extra: dev
Requires-Dist: black; extra == 'dev'
Requires-Dist: build; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Description-Content-Type: text/markdown

# AlgoTradeKit

**Algorithmic Trading Toolkit** — collect market data, build indicators and strategies, backtest, and trade live.

```
pip install AlgoTradeKit
```

---

## Modules

| Module      | Status      | Description                                            |
| ----------- | ----------- | ------------------------------------------------------ |
| `data`      | ✅ v0.1.0   | Collect OHLCV candles from exchanges                   |
| `indicator` | ✅ v0.4.0   | RSI, MACD, MA family, Ichimoku — TradingView-compatible|
| `visual`    | ✅ v0.3.0   | Candlestick charts, indicators, live streaming         |
| `strategy`  | 🔜 planned  | Build and combine trading strategies                   |
| `simulate`  | 🔜 planned  | Backtest strategies on historical data                 |
| `trade`     | 🔜 planned  | Live trading via exchange API or MT5                   |

---

## Quick Start

### Collect candle data

```python
from AlgoTradeKit.data import Collector

collector = Collector(source="binance-futures", symbol="BTCUSDT", timeframe="1d")
collector.destination = "data/"
collector.starttime   = "2020/01/01"
collector.collect()
```

### Compute indicators

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

df = pd.read_csv("data/binance-futures_BTCUSDT_1d.csv")

# RSI (default: length=14, OB=70, OS=30)
rsi = RSI(df["close"])
print(rsi.rsi.tail())

# RSI with MA overlay
rsi_ma = RSI(df["close"], length=14, show_ma=True, ma_type="EMA", ma_length=9)

# MACD (default: 12, 26, 9)
macd = MACD(df["close"])
print(macd.macd.tail(), macd.signal.tail(), macd.histogram.tail())

# EMA
ema20 = EMA(df["close"], length=20)

# Ichimoku (all default TradingView settings)
ichi = Ichimoku(df["high"], df["low"], df["close"])
print(ichi.tenkan.tail())
print(ichi.cloud_df().tail(30))  # includes 26 future bars
```

### Visualise with indicators

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

chart = Chart.from_csv("data/binance-futures_BTCUSDT_1d.csv")

rsi  = RSI(chart.df["close"])
macd = MACD(chart.df["close"])
ema  = EMA(chart.df["close"], length=20)
ichi = Ichimoku(chart.df["high"], chart.df["low"], chart.df["close"])

add_ma(chart,       ema,  timestamps=chart.df["timestamp"])
add_ichimoku(chart, ichi, timestamps=chart.df["timestamp"])
add_rsi(chart,      rsi,  timestamps=chart.df["timestamp"])
add_macd(chart,     macd, timestamps=chart.df["timestamp"])

chart.show()   # opens browser at http://localhost:9000
```

| Feature            | Details                                                                     |
|--------------------|-----------------------------------------------------------------------------|
| Ichimoku cloud     | Canvas-drawn fill between Span A/B — bullish teal / bearish red             |
| Indicator grouping | Multi-line indicators grouped with collapsible header, hide-all, remove-all |
| Legend position    | Top-left of chart pane                                                      |
| Settings ⚙         | Font, axis label toggles (last close / indicator prices on right axis)      |
| Cursor axis label  | Always shows candle close price, never indicator values                     |

---

## Data Module

### Collect candle data

```python
from AlgoTradeKit.data import Collector

collector = Collector(source="binance-spot", symbol="ETHUSDT", timeframe="4h")
collector.destination = "data/"
collector.starttime   = "2021/01/01"
collector.endtime     = "2023/01/01"   # optional — defaults to now
collector.collect()                     # returns path to saved CSV
```

### Resample timeframes

```python
from AlgoTradeKit.data import Converter

conv = Converter(source="data/binance-futures_BTCUSDT_1h.csv", target_timeframe="4h")
conv.destination = "data/"
conv.convert()
```

### Supported sources

| Source key            | Market                |
| --------------------- | --------------------- |
| `"binance-spot"`      | Binance Spot          |
| `"binance-futures"`   | Binance USD-M Futures |

### Supported timeframes

`1m` `3m` `5m` `15m` `30m` `1h` `2h` `4h` `6h` `8h` `12h` `1d` `3d` `1w` `1M`

---

## Indicator Reference

### MA Family

All Moving Averages accept `source` (price series) and `length` (period).

| Class    | Formula                              | TV Default | TV Colour  |
| -------- | ------------------------------------ | ---------- | ---------- |
| `SMA`    | Rolling mean                         | 9          | `#2962FF`  |
| `EMA`    | α = 2/(n+1)                          | 9          | `#FF6D00`  |
| `WMA`    | Linear weights                       | 9          | `#00BCD4`  |
| `SMMA`   | Wilder RMA, α = 1/n                  | 9          | `#4CAF50`  |
| `DEMA`   | 2·EMA − EMA(EMA)                     | 9          | `#F44336`  |
| `TEMA`   | 3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))  | 9          | `#FF9800`  |
| `HullMA` | WMA(2·WMA(n/2)−WMA(n), √n)          | 9          | `#9C27B0`  |
| `VWMA`   | Σ(price·vol) / Σ(vol)                | 20         | `#E040FB`  |
| `VWAP`   | Cumulative PV/V with σ bands         | —          | `#2962FF`  |

```python
from AlgoTradeKit.indicator import SMA, EMA, WMA, VWMA, SMMA, DEMA, TEMA, HullMA, VWAP

sma  = SMA(close, length=20)
ema  = EMA(close, length=20)
wma  = WMA(close, length=20)
vwma = VWMA(close, volume, length=20)
smma = SMMA(close, length=20)
dema = DEMA(close, length=20)
tema = TEMA(close, length=20)
hma  = HullMA(close, length=20)
vwap = VWAP(high, low, close, volume, anchor="none", bands=[1, 2])
```

### RSI

```python
rsi = RSI(
    source      = df["close"],
    length      = 14,          # period (TV default)
    overbought  = 70,          # upper level (TV default)
    oversold    = 30,          # lower level (TV default)
    show_ma     = False,       # add MA on RSI line
    ma_type     = "EMA",       # EMA | SMA | SMMA | WMA
    ma_length   = 14,
)

rsi.rsi                    # RSI series
rsi.is_overbought()        # bool series
rsi.is_oversold()          # bool series
rsi.crossover_ob()         # entered overbought
rsi.crossunder_os()        # exited oversold (bullish signal)
```

### MACD

```python
macd = MACD(
    source        = df["close"],
    fast_length   = 12,        # TV default
    slow_length   = 26,        # TV default
    signal_length = 9,         # TV default
    oscillator_ma = "EMA",     # EMA | SMA | SMMA | WMA
    signal_ma     = "EMA",     # EMA | SMA | SMMA | WMA
)

macd.macd                  # MACD line
macd.signal                # Signal line
macd.histogram             # MACD − Signal
macd.histogram_colors()    # per-bar TV 4-colour Series
macd.crossover()           # bullish MACD/signal cross
macd.zero_crossover()      # MACD crosses above zero
```

### Ichimoku

```python
ichi = Ichimoku(
    high            = df["high"],
    low             = df["low"],
    close           = df["close"],
    tenkan_period   = 9,        # TV default
    kijun_period    = 26,       # TV default
    senkou_b_period = 52,       # TV default
    displacement    = 26,       # TV default
)

ichi.tenkan                # Conversion Line
ichi.kijun                 # Base Line
ichi.senkou_a              # Leading Span A  (26 bars forward)
ichi.senkou_b              # Leading Span B  (26 bars forward)
ichi.chikou                # Lagging Span    (26 bars back)
ichi.cloud_df()            # DataFrame with full cloud + 26 future bars
ichi.tk_cross_bullish()    # Tenkan crosses above Kijun
ichi.price_above_cloud()   # Close above both Span A and B
```

---

## 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]"
```

---

## Requirements

- `pandas >= 2.0`
- `requests >= 2.28`
- `fastapi >= 0.110.0` *(visual module)*
- `uvicorn >= 0.29.0` *(visual module)*
- `websockets >= 12.0` *(visual module)*

---

## Roadmap

- [x] `data` module — Collector (Binance Spot & Futures)
- [x] `data` module — Converter (timeframe resampling)
- [x] `visual` module — interactive candlestick chart
- [x] `indicator` module — RSI, MACD, MA family, Ichimoku
- [ ] `indicator` — Bollinger Bands, ATR, Stochastic, VWAP session anchor
- [ ] `strategy` module — strategy builder with entry/exit logic
- [ ] `simulate` module — backtesting engine with full report
- [ ] `trade` module — live trading via exchange API / MT5
- [ ] MEXC, Bybit, OKX data sources

---

## License

MIT — see [LICENSE](LICENSE)
