pip3 install git+git://github.com/anfederico/gemini.githttps://github.com/anfederico/gemini/issues
from gemini_core import data, engine, helpers- Basic mean reversion strategy
- Automated cryptocurrency trading bot deployed into AWS with Terraform
- For more information, please refer to the full documentation*.
If you have your own data that has/hasn't been processed, you should conform to the following structure. Basically, load your data into a Pandas dataframe object and be sure to convert the dates to datetime format and include the following lowercase column titles.
date high low open close
0 2017-07-08 11:00:00 2480.186778 2468.319314 2477.279567 2471.314030
1 2017-07-08 11:30:00 2471.314030 2455.014057 2471.202796 2458.073602
2 2017-07-08 12:00:00 2480.000000 2456.000000 2458.073602 2480.000000
3 2017-07-08 12:30:00 2489.004639 2476.334333 2479.402768 2481.481258
4 2017-07-08 13:00:00 2499.000000 2476.621873 2481.458643 2491.990000
5 2017-07-08 13:30:00 2503.503479 2490.314610 2492.440289 2496.005562
6 2017-07-08 14:00:00 2525.000000 2491.062741 2494.449524 2520.775500
7 2017-07-08 14:30:00 2521.500036 2510.000000 2520.775500 2518.450645
8 2017-07-08 15:00:00 2519.817394 2506.054360 2518.451000 2514.484009
4195.81 ┤
4161.76 ┤ ╭─╮
4127.72 ┤ │ ╰╮
4093.67 ┤ │ │
4059.62 ┤ ╭╮ │ ╰╮
4025.58 ┤ ╭─╯╰╮╭╮ │ ╰─╮
3991.53 ┤ ╭╯ ╰╯│ ╭╮╭╮ │ │
3957.48 ┤ │ ╰╮ ╭╯╰╯│╭╮│ │
3923.44 ┤ │ │ │ ╰╯╰╯ │
3889.39 ┤ │ │ ╭╮╭╯ │ ╭───╮
3855.34 ┤ │ │ ╭╯││ │ │ ╰─╮
3821.30 ┤ ╭╮ │ ╰──╯ ╰╯ │ ╭╮ ╭╮ ╭╯ ╰╮ ╭╮ ╭╮ ╭
3787.25 ┤ ││ │ │╭╮ ╭╯╰╮ ╭╯│ │ │ │╰─╯╰─╮ │
3753.21 ┤ ╭╮│╰─╮ │ ││╰╮╭╯ ╰╮│ │╭──╮ │ │ ╭╯ ╰─╮ │
3719.16 ┤ │╰╯ │╭─╯ ╰╯ ╰╯ ╰╯ ╰╯ │ │ ╰─╯ │ ╭─╮╭╯
3685.11 ┤ ╭╯ ╰╯ │ │ ╰──╯ ╰╯
3651.07 ┤ │ │ ╭╯
3617.02 ┤ │ ╰╮╭─╯
3582.97 ┤ │ ╰╯
3548.93 ┤╮ ╭╯
3514.88 ┼╰╮╭╯
3480.83 ┤ ╰╯
If you don't have your own data, we've included useful functions for grabbing low and high timeframe historical data from crypto exchanges. These helper functions will automatically resample your datasets to any desired timeframe and return a Gemini-compatible dataframe.
# Higher timeframes (>= daily)
df = data.get_htf_candles("BTC_USD", "Bitfinex", "3-DAY", "2019-01-12 00:00:00", "2019-02-01 00:00:00")
# Lower timeframes (< daily)
df = data.get_ltf_candles("USDC_BTC", "30-MIN", "2019-01-12 00:00:00", "2019-02-01 00:00:00")backtest = engine.backtest(df)In addition to loading the data, you must define the strategy you want to test. To do this, we'll create a logic function that can be passed to the backtester when you start. The backtester will proceed step-wise through the dataset, copying the current/past datapoints into a variable called "Lookback" to prevent lookahead bias. If the data hasn't already been processed, you may process it within the logic function (this makes the simulation more accurate but significantly increases runtime). You can then use the helper class called "Period" to conveniently reference current and past datapoints. With those, you may execute long, sell, short, and cover positions directly on the "Account" class based on your strategy.
def logic(account, lookback):
try:
# Process dataframe to collect signals
lookback = helpers.get_signals(lookback)
# Load into period class to simplify indexing
lookback = helpers.period(lookback)
today = lookback.loc(0) # Current candle
yesterday = lookback.loc(-1) # Previous candle
if today['signal'] == "down":
if yesterday['signal'] == "down":
exit_price = today['close']
for position in acount.positions:
if position.type == 'long':
account.close_position(position, 0.5, exit_price)
if today['signal'] == "up":
if yesterday['signal'] == "up":
risk = 0.03
entry_price = today['close']
entry_capital = account.buying_power*risk
if entry_capital >= 0:
account.enter_position('long', entry_capital, entry_price)
except ValueError:
pass # Handles lookback errors in beginning of dataset
# Start backtesting custom logic with 1000 (BTC) intital capital
backtest.start(1000, logic)After the backtest, you can analyze your strategy by printing the results to console. As of now, these include simple statistics of your run but we plan to implement more complicated metrics for a stronger understanding of performance.
backtest.results()Buy and Hold : -3.03%
Net Profit : -30.26
Strategy : 40.0%
Net Profit : 400.01
Longs : 156
Sells : 137
Shorts : 0
Covers : 0
--------------------
Total Trades : 293
You can visualize the performance of your strategy by comparing the equity curve with a buy and hold baseline. The equity curve simply tracks your account value throughout the backtest and will optionally show where your algorithm made its trades including longs, sells, shorts, and covers.
backtest.chart()