# Octobot-Pro [0.0.11](https://github.com/Drakkar-Software/OctoBot-Pro/tree/master/CHANGELOG.md)
[](https://pypi.python.org/pypi/octobot-pro/)
[](https://pepy.tech/project/octobot-pro)
[](https://github.com/Drakkar-Software/OctoBot-Pro/actions)
## Octobot-Pro Community
[](https://octobot.click/readme-telegram-octobot-pro)
[](https://octobot.click/gh-discord-octobot-pro)
[](https://octobot.click/gh-twitter-octobot-pro)
## OctoBot Pro is the backtesting framework using the OctoBot Ecosystem
> OctoBot Pro is in early alpha version
Documentation available at [https://pro.octobot.info/](https://octobot.click/Xzae1a6)
## Install OctoBot Pro from pip
> OctoBot pro requires **Python 3.10**
``` {.sourceCode .bash}
python3 -m pip install OctoBot wheel
python3 -m pip install octobot-pro
```
## Example of a backtesting script
### Script
``` python
import asyncio
import tulipy # Can be any TA library.
import octobot_pro as op
async def rsi_test():
async def strategy(ctx):
# Will be called at each candle.
if run_data["entries"] is None:
# Compute entries only once per backtest.
closes = await op.Close(ctx, max_history=True)
times = await op.Time(ctx, max_history=True, use_close_time=True)
rsi_v = tulipy.rsi(closes, period=ctx.tentacle.trading_config["period"])
delta = len(closes) - len(rsi_v)
# Populate entries with timestamps of candles where RSI is
# bellow the "rsi_value_buy_threshold" configuration.
run_data["entries"] = {
times[index + delta]
for index, rsi_val in enumerate(rsi_v)
if rsi_val < ctx.tentacle.trading_config["rsi_value_buy_threshold"]
}
await op.plot_indicator(ctx, "RSI", times[delta:], rsi_v, run_data["entries"])
if op.current_live_time(ctx) in run_data["entries"]:
# Uses pre-computed entries times to enter positions when relevant.
# Also, instantly set take profits and stop losses.
# Position exists could also be set separately.
await op.market(ctx, "buy", amount="10%", stop_loss_offset="-15%", take_profit_offset="25%")
# Configuration that will be passed to each run.
# It will be accessible under "ctx.tentacle.trading_config".
config = {
"period": 10,
"rsi_value_buy_threshold": 28,
}
# Read and cache candle data to make subsequent backtesting runs faster.
data = await op.get_data("BTC/USDT", "1d", start_timestamp=1505606400)
run_data = {
"entries": None,
}
# Run a backtest using the above data, strategy and configuration.
res = await op.run(data, strategy, config)
print(res.describe())
# Generate and open report including indicators plots
await res.plot(show=True)
# Stop data to release local databases.
await data.stop()
# Call the execution of the script inside "asyncio.run" as
# OctoBot-Pro runs using the python asyncio framework.
asyncio.run(rsi_test())
```
### Generated report
### Join the community
We recently created a telegram channel dedicated to OctoBot Pro.
[](https://octobot.click/readme-telegram-octobot-pro)
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"description": "# Octobot-Pro [0.0.11](https://github.com/Drakkar-Software/OctoBot-Pro/tree/master/CHANGELOG.md)\n[](https://pypi.python.org/pypi/octobot-pro/)\n[](https://pepy.tech/project/octobot-pro)\n[](https://github.com/Drakkar-Software/OctoBot-Pro/actions)\n\n## Octobot-Pro Community\n[](https://octobot.click/readme-telegram-octobot-pro)\n[](https://octobot.click/gh-discord-octobot-pro)\n[](https://octobot.click/gh-twitter-octobot-pro)\n\n\n## OctoBot Pro is the backtesting framework using the OctoBot Ecosystem\n\n> OctoBot Pro is in early alpha version\n\nDocumentation available at [https://pro.octobot.info/](https://octobot.click/Xzae1a6)\n\n\n## Install OctoBot Pro from pip\n\n> OctoBot pro requires **Python 3.10**\n\n``` {.sourceCode .bash}\npython3 -m pip install OctoBot wheel\npython3 -m pip install octobot-pro\n```\n\n## Example of a backtesting script\n\n### Script\n``` python\nimport asyncio\nimport tulipy # Can be any TA library.\nimport octobot_pro as op\n\n\nasync def rsi_test():\n async def strategy(ctx):\n # Will be called at each candle.\n if run_data[\"entries\"] is None:\n # Compute entries only once per backtest.\n closes = await op.Close(ctx, max_history=True)\n times = await op.Time(ctx, max_history=True, use_close_time=True)\n rsi_v = tulipy.rsi(closes, period=ctx.tentacle.trading_config[\"period\"])\n delta = len(closes) - len(rsi_v)\n # Populate entries with timestamps of candles where RSI is\n # bellow the \"rsi_value_buy_threshold\" configuration.\n run_data[\"entries\"] = {\n times[index + delta]\n for index, rsi_val in enumerate(rsi_v)\n if rsi_val < ctx.tentacle.trading_config[\"rsi_value_buy_threshold\"]\n }\n await op.plot_indicator(ctx, \"RSI\", times[delta:], rsi_v, run_data[\"entries\"])\n if op.current_live_time(ctx) in run_data[\"entries\"]:\n # Uses pre-computed entries times to enter positions when relevant.\n # Also, instantly set take profits and stop losses.\n # Position exists could also be set separately.\n await op.market(ctx, \"buy\", amount=\"10%\", stop_loss_offset=\"-15%\", take_profit_offset=\"25%\")\n\n # Configuration that will be passed to each run.\n # It will be accessible under \"ctx.tentacle.trading_config\".\n config = {\n \"period\": 10,\n \"rsi_value_buy_threshold\": 28,\n }\n\n # Read and cache candle data to make subsequent backtesting runs faster.\n data = await op.get_data(\"BTC/USDT\", \"1d\", start_timestamp=1505606400)\n run_data = {\n \"entries\": None,\n }\n # Run a backtest using the above data, strategy and configuration.\n res = await op.run(data, strategy, config)\n print(res.describe())\n # Generate and open report including indicators plots \n await res.plot(show=True)\n # Stop data to release local databases.\n await data.stop()\n\n\n# Call the execution of the script inside \"asyncio.run\" as\n# OctoBot-Pro runs using the python asyncio framework.\nasyncio.run(rsi_test())\n```\n\n### Generated report\n\n\n### Join the community\nWe recently created a telegram channel dedicated to OctoBot Pro.\n\n[](https://octobot.click/readme-telegram-octobot-pro)\n",
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