mdfft 1.0.8

Market Data Form Finder Toolkit (MDFFT). The project goal is to make working with a raw market data easier.

Market Data Form Finder Toolkit (MDFFT)

The project goal is to make working with a raw market data easier. Provide a tool that allow to identify recurrent market patterns. Any types of market can processed by this.

The library allow to carry out following tasks:

• Load a list of quotes for any instrument.
• Create a chart using the Japanese candlestick format.
• Draw lines and markers on the chart.
• Search for candles using various criteria.
• Navigate through the entire candles collection.
• Change the timeframe to different one.

Table of content

• Install
• Import classes
• Candle collection examples
  • Load candles collection
  • Candles count
  • First and last candles
  • Get candle by index
  • Navigation through dataset
  • Search candle by date
  • Subcollection by index
  • Subcollection by date
  • Iteration over collection
  • High and low candles in collection
• Timeframes
  • Timeframe of candles collection
  • Change timeframe
• Pandas
  • Create pandas dataset from collection
• Candle
  • Prev and next candle
  • Candle type
  • Candle prices
  • Candle time
  • Candle index
• Candle chart
  • Simple chart
  • Save chart to file
  • Chart appearance
  • Draw on chart
• Trading

Install

 pip install mdfft

Import classes

The namespace mdfft contains of whole list of lib's classes. The example of import is below:

from mdfft import Candles, SimpleParser, Styler, painter as p, RawCandle, Trader

Classes:

• Candle - The class represents a Japanese candle
• Candles - The class represents a Japanese candle list
• RawCandle - The simple class that based to create Candle. May used in quote parsers.
• painter - The object to draw a chart.
• Style - The class to appearance a chart.
• SimpleParser - The class to parse candles for my samples.

Candle collection examples

Load candles

The collection of candles is created by parsing quotes or making an array of RawCandle objects. The array of RawCandles is created by the programmer himself.

   raw_candles = [
        RawCandle(
            dt=datetime(2020, 1, 1), # Candle datetime
            o=2,                     # Open price
            h=4,                     # High price
            l=1,                     # Low price
            c=3                      # Close price
        ),
        RawCandle(
            dt=datetime(2020, 1, 2), # dt
            o=12,                    # ohlc prices
            h=14,
            l=11,
            c=13
        )
    ]
    candles = Candles.from_raw_candles(raw_candles, timeframe="1D")
    print(candles)

Next I will use my SimpleParser and my rather large data set of OHLC quotes.

 parser = SimpleParser(file="./data/EURUSD_H1.txt")
 candles = Candles.from_raw_candles(parser.parse(), timeframe="1H")

Count of candles in the dataset

    print(candles.candles_count())
    print(len(candles))

A first and a last candle in the dataset

    start_candle = candles.head()
    print("Start candle: " + str(start_candle))

    end_candle = candles.tail()
    print("End candle: " + str(end_candle))

Get a candle by an index

    my_candle = candles.candle_at(1985)
    same_candle = candles[1985]

Navigation through a dataset

Navigation is performed by calling next() and prev() methods of a candle object.

    next_candle = my_candle.next()
    prev_candle = my_candle.prev()
    next20_candle = my_candle.next(20)
    prev30_candle = my_candle.prev(30)

Search by a date

Exact search

    found_candle = candles.find_candle(datetime(1999, 1, 4, 5))
    if not found_candle:
        print("Candle not found at 1999-01-04 05:00")
    return print("Found candle: " + str(found_candle))

Finds a candle whose date is greater than the given

    found_candle = candles.find_candle(datetime(1999, 1, 4, 6), nearly=True)
    return print("Found candle: " + str(found_candle))

Get a subcollection from the main collection by indices

    # take 10 skip 5 candles
    print(candles.take(10, skip=5))
    # or  print(candles[5:15])

    # skip 5 candles from collection
    print(candles.skip(5))
    # or  print(candles[5:])

Get a subcollection from the main collection by dates

    from datetime import datetime
    # New candles collection from 2000-01-01 to 2000-01-10
    # Note Only if candles are exists
    candles_range = candles.range_dt( datetime(2000, 1, 3,  1 ), datetime(2000, 1, 10, 1))
    if not candles_range:
        print("No candles in range")
        return 0
    print("Count candles: " + str(candles_range.candles_count()))
    print(candles_range)

Iterate over a collection

    # Iterate candles
    for candle in candles:
        print("Candle: " + str(candle))

A highest and a lowest candle in the collection

    # Get the high candle and the low candle
    candle_h, candle_l = candles.high_and_low_candles()
    print("High candle: " + str(candle_h))
    print("Low candle: " + str(candle_l))

Timeframes

A candle collection may be converted from a current timeframe to another timeframe. A new timeframe must be bigger than the source timeframe.

Example: the timeframe of 1 hour may be converted to 2 hours, 8 hours, 1 day, or another one, but bigger than source one. The 1 hour timeframe must not convert to 45 minutes, 30 minutes, or another one less.

A timeframe label consists of two parts: a number of time units and a time unit label.

Example of timeframes: 20M - 29 minutes timeframe, 1H - 1 hour timeframe, 3H - 11 hours timeframe, 1D - 1 day timeframe

Possible time unit labels:

• M - minute
• H - hour
• D - day
• W - week
• MONTH - month

Possible timeframes units:

• For minute - 1,2,3,4,5,6,10,15,20,30
• For hours - 1,2,3,4,6,8,12
• Other - only 1

Get a timeframe of a candles collection

    print(candles.timeframe)

Change a candles collection timeframe

    candles_3h  = candles_1h.to_tf("3H")
    candles_4h  = candles_3h.to_tf("4H")
    candles_12h = candles_4h.to_tf("12H")
    candles_1d  = candles_4h.to_tf("1D")

Pandas

Convert a candles collection to a pandas dataframe

    from mdfft import PandasCandles

    df = PandasCandles.candles_to_pandas_df(candles)
    print(df)

You may create a dataset in which a row contains several candles prices.

    from mdfft import PandasCandles

    # 3 candles in a single row
    df_windows = PandasCandles.candles_windows_to_pandas_df(candles, 3)
    print(df_windows)

Candle

Next and prev candle

    # Candle at 1985 index
    candle = candles.candle_at(1985)

    # Next candle
    next_candle = candle.next()

    # 30 candles to forward
    next30_candle = candle.next(30)

    # Prev candle
    prve_candle = candle.prev()

    # Prev 100 candle
    prev100_candle = candle.prev(100)

Candle type

    # bull/bear/doji see constants in Candle.DIRECT_*
    direct = candle.direct # or candle.d or candle['d']  print(direct)

Prices

    # All prices different methods
    price_open = candle.open   # or candle.o or candle['o']
    price_high = candle.high   # or candle.h or candle['h']
    price_low = candle.low     # or candle.l or candle['l']
    price_close = candle.close # or candle.c or candle['c']
    price_mid = candle.mid     # or candle.m or candle['m']

Dates and times

    # Returns datetime object
    candle_dt = candle.datetime # or candle.dt or candle['dt']
    candle_date = candle.date
    candle_time = candle.time

    # Returns int
    candle_year   = candle.year
    candle_month  = candle.month
    candle_day    = candle.day
    candle_hour   = candle.hour
    candle_minute = candle.minute

    # Date time format
    dt_str = candle.dt_as_str()
    print(dt_str)

    dt_str_fmt = candle.dt_as_str('%d, %b %Y')
    print(dt_str_fmt)

    # Timestamp
    ts = candle.timestamp()
    print(ts)

    ts_utc = candle.timestamp_utc()
    print(ts_utc)

Candle index

    # Candle index from start
    ci = candle.index # or candle.i or candle['i']

    # Candle index from back
    cib = candle.index_back # or candle.ib or candle['ib']

Candle chart

To start painting you must import bellow classes

    from mdfft import painter as p, Styler

Matplotlib is used for drawing. That means you may use colors, marker, visual elements, etc. from Matplotlib.

Simple chart

    p.start_paint()
    p.title="Candles"
    p.paint(candles_collection)
    p.paint_done()

Save chart to file

    p.start_paint()
    p.title = "Save paint"
    p.paint(candles_to_paint)
    p.save_paint('candles.jpg')

Appearance

The appearance is performed through the object of class Styler that included in object painter. Possible settings is an enum in class Styler.

    # The appearance of the chart is configured through the object Styler
    # Each candle has own the color of body, shadow and border
    s = Styler()
    p.title="Rainbow candles"
    s.color_bear_body =   [plt.cm.get_cmap('Pastel1', candles_cnt)(c) for c in range(candles_cnt)]
    s.color_bear_border = [plt.cm.get_cmap('Set1_r',  candles_cnt)(c) for c in range(candles_cnt)]
    s.color_bear_shadow = "blue"
    s.color_bull_body =   [plt.cm.get_cmap('turbo',   candles_cnt)(c) for c in range(candles_cnt)]
    s.color_bull_border = [plt.cm.get_cmap('tab20b',  candles_cnt)(c) for c in range(candles_cnt)]
    s.color_bull_shadow = "green"

    # Apply style
    p.styler = s

    # Draw candles
    p.start_paint()
    p.paint(candles)
    p.paint_done()

Drawing on the chart

Possible markers and lines

    p.start_paint()

    # Paint candles
    p.paint(candles_to_paint)

    # Paint markers for each 5 candles
    for c in candles_to_paint:
        if (c.index % 5) == 0:
            p.paint_marker(c, "l", marker_size=4, marker_color='red', marker='v')
            p.paint_marker(c, "h", marker_size=3, marker_color='black', marker='^')
            p.paint_marker(c, c.mid, marker='_')

    # Paint line. From hightest price to lowest price
    hc, lc = candles_to_paint.high_and_low_candles()
    p.paint_line( hc, hc.high, lc, lc.low, line_style="dotted", line_color="green")

    # Show picture
    p.paint_done()

Trading

For the success or failure of a position, the Trader class is used. The class contains a single method that returns the result of a position at a certain point.

    pip = 1 / 10000
    start_candle = candles.candle_at(12345)
    trader = Trader()

    profit, distance, action_price = trader.place_order(
        candle=start_candle,              # candle for order, not datetime
        order_price='o',                  # open price - possible 'o' 'h' 'l' 'c' OR float
        place_type=Trader.PLACE_TYPE_BUY, # PLACE_TYPE_BUY or PLACE_TYPE_SELL
        sl=start_candle.low - pip,        # stop loss
        tp=start_candle.high + pip * 100, # take profit
        tl=1000                           # candles before force close. time to live position
    )

    # My profit. Positive or negative
    print(f"Profit pips: {profit / pip}")

    # Candles from start before close order
    print(f"Candles to close order: {distance}")

    # Price that close order
    print(f"Price that close order: {action_price}")