OctoBot Pro
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Strategy basis

On OctoBot-Pro, a trading strategy is a python async function that will be called at new price data.
async def strategy(ctx):
# your strategy content
In most cases, a strategy will:
  1. 1.
    Read price data
  2. 2.
    Use technical evaluators or statistics
  3. 3.
    Decide to take (or not take) action depending on its configuration
  4. 4.
    Create / cancel or edit orders (see Creating orders)
As OctoBot-Pro strategies are meant for backtesting, it is possible to create a strategy in 2 ways:

Pre-computed strategies

Pre-computed are only possible in backtesting: since the data is already known, when dealing with technical evaluator based strategies, it is possible to compute the values of the evaluators for the whole backtest at once. This approach is faster than iterative strategies as evaluators call only called once.
Warning: when writing a pre-computed strategy, always make sure to associate the evaluator values to the right time otherwise you might be reading data from the past of the future when running the strategy.
config = {
"period": 10,
"rsi_value_buy_threshold": 28,
}
run_data = {
"entries": None,
}
async def strategy(ctx):
if run_data["entries"] is None:
# 1. Read price data
closes = await op.Close(ctx, max_history=True)
times = await op.Time(ctx, max_history=True, use_close_time=True)
# 2. Use technical evaluators or statistics
rsi_v = tulipy.rsi(closes, period=ctx.tentacle.trading_config["period"])
delta = len(closes) - len(rsi_v)
# 3. Decide to take (or not take) action depending on its 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"]:
# 4. Create / cancel or edit orders
await op.market(ctx, "buy", amount="10%", stop_loss_offset="-15%", take_profit_offset="25%")
This pre-computed strategy computes entries using the RSI: times of favorable entries are stored into run_data["entries"] which is defined outside on the strategy function in order to keep its values throughout iterations.
Please note the max_history=True in op.Close and op.Time keywords. This is allowing to select data using the whole run available data and only call tulipy.rsi once and populate run_data["entries"] only once.
In each subsequent call, run_data["entries"] is None will be True and only the last 2 lines of the strategy will be executed.

Iterative strategies

config = {
"period": 10,
"rsi_value_buy_threshold": 28,
}
async def strategy(ctx):
# 1. Read price data
close = await op.Close(ctx)
if len(close) <= ctx.tentacle.trading_config["period"]:
# not enough data to compute RSI
return
# 2. Use technical evaluators or statistics
rsi_v = tulipy.rsi(close, period=ctx.tentacle.trading_config["period"])
# 3. Decide to take (or not take) action depending on its configuration
if rsi_v[-1] < ctx.tentacle.trading_config["rsi_value_buy_threshold"]:
# 4. Create / cancel or edit orders
await op.market(ctx, "buy", amount="10%", stop_loss_offset="-15%", take_profit_offset="25%")
This iterative strategy is similar to the above pre-computed strategy except that it is evaluating the RSI at each candle to know if an entry should be created.
This type of strategy is simpler to create than a pre-computed strategy and can be used in OctoBot live trading.

Running a strategy

When running a backtest, a strategy should be referenced alongside:
res = await op.run(data, strategy, config)
Have a look here for a full example of how to run a strategy within a python script.