CSVDataFeed for Histdata.com¶

Raw histdata.com data¶

2024.12.23,00:00,2625.465000,2625.525000,2625.065000,2625.075000,0
2024.12.23,00:01,2625.005000,2625.345000,2625.005000,2625.215000,0
2024.12.23,00:02,2625.225000,2625.288000,2624.925000,2625.115000,0
2024.12.23,00:03,2625.115000,2625.125000,2624.798000,2625.115000,0
2024.12.23,00:04,2625.115000,2625.515000,2625.115000,2625.415000,0

Method 1: Load CSV¶

csv parameters is reflect of pandas.read_csv() parameters

In [9]:

from lettrade.exchange.backtest import CSVBackTestDataFeed
import warnings

warnings.simplefilter(action="ignore", category=FutureWarning)

df = CSVBackTestDataFeed(
    "example/data/data/DAT_MT_XAUUSD_M1_202412.csv",
    csv=dict(
        # skiprows=[0],
        names=["date", "time", "open", "high", "low", "close", "volumn"],
        header=None,
        delimiter=",",
        parse_dates={"datetime": [0, 1]},
        date_format="%Y%m%d %H%M%S",
    ),
    name="AUDUSD",
    timeframe="1m",
)

# df = df[df.high != df.low]

df

from lettrade.exchange.backtest import CSVBackTestDataFeed import warnings warnings.simplefilter(action="ignore", category=FutureWarning) df = CSVBackTestDataFeed( "example/data/data/DAT_MT_XAUUSD_M1_202412.csv", csv=dict( # skiprows=[0], names=["date", "time", "open", "high", "low", "close", "volumn"], header=None, delimiter=",", parse_dates={"datetime": [0, 1]}, date_format="%Y%m%d %H%M%S", ), name="AUDUSD", timeframe="1m", ) # df = df[df.high != df.low] df

Out[9]:

	open	high	low	close	volumn
datetime
2024-12-23 00:00:00+00:00	2625.465	2625.525	2625.065	2625.075	0
2024-12-23 00:01:00+00:00	2625.005	2625.345	2625.005	2625.215	0
2024-12-23 00:02:00+00:00	2625.225	2625.288	2624.925	2625.115	0
2024-12-23 00:03:00+00:00	2625.115	2625.125	2624.798	2625.115	0
2024-12-23 00:04:00+00:00	2625.115	2625.515	2625.115	2625.415	0
...	...	...	...	...	...
2024-12-12 23:54:00+00:00	2687.195	2687.445	2687.155	2687.365	0
2024-12-12 23:55:00+00:00	2687.365	2687.425	2686.985	2687.035	0
2024-12-12 23:56:00+00:00	2687.035	2687.105	2686.895	2686.985	0
2024-12-12 23:57:00+00:00	2686.985	2686.985	2686.605	2686.825	0
2024-12-12 23:58:00+00:00	2686.915	2687.335	2686.825	2687.085	0

28763 rows × 5 columns

Method 2: load from pandas data¶

In [10]:

import pandas as pd
from lettrade.exchange.backtest import BackTestDataFeed

# Load the CSV file into a DataFrame
df = pd.read_csv(
    "example/data/data/DAT_MT_XAUUSD_M1_202412.csv",
    names=["date", "time", "open", "high", "low", "close", "volumn"],
    header=None,
    delimiter=",",
)
df.index = pd.to_datetime(df.date + " " + df.time)
df.drop(["date", "time"], axis=1, inplace=True)
df.sort_index(inplace=True)

# Create a DataFeed
data = BackTestDataFeed(
    df,
    name="XAUUSD",
    timeframe="1m",
)
data

import pandas as pd from lettrade.exchange.backtest import BackTestDataFeed # Load the CSV file into a DataFrame df = pd.read_csv( "example/data/data/DAT_MT_XAUUSD_M1_202412.csv", names=["date", "time", "open", "high", "low", "close", "volumn"], header=None, delimiter=",", ) df.index = pd.to_datetime(df.date + " " + df.time) df.drop(["date", "time"], axis=1, inplace=True) df.sort_index(inplace=True) # Create a DataFeed data = BackTestDataFeed( df, name="XAUUSD", timeframe="1m", ) data

Out[10]:

	open	high	low	close	volumn
datetime
2024-12-01 18:00:00	2653.075	2653.115	2648.748	2650.295	0
2024-12-01 18:01:00	2650.248	2650.855	2648.598	2649.888	0
2024-12-01 18:02:00	2650.035	2650.235	2648.345	2649.938	0
2024-12-01 18:03:00	2649.538	2649.768	2648.124	2649.538	0
2024-12-01 18:04:00	2649.338	2649.338	2648.268	2648.658	0
...	...	...	...	...	...
2024-12-31 16:54:00	2624.365	2624.385	2624.355	2624.355	0
2024-12-31 16:55:00	2624.385	2624.385	2624.105	2624.245	0
2024-12-31 16:56:00	2624.225	2624.265	2624.205	2624.265	0
2024-12-31 16:57:00	2624.305	2624.365	2623.908	2624.155	0
2024-12-31 16:58:00	2624.005	2624.255	2623.998	2624.255	0

28763 rows × 5 columns

Plot¶

Init Plotly environment¶

In [11]:

import plotly.io as pio

pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"

import plotly.io as pio pio.renderers.default = "notebook" pio.templates.default = "plotly_dark"

Show¶

In [12]:

import plotly.graph_objects as go

fig = go.Figure(
    data=[
        go.Candlestick(
            x=df.index,
            open=df.open,
            high=df.high,
            low=df.low,
            close=df.close,
        )
    ]
)

fig.show()

import plotly.graph_objects as go fig = go.Figure( data=[ go.Candlestick( x=df.index, open=df.open, high=df.high, low=df.low, close=df.close, ) ] ) fig.show()