TTI (Trading Technical Indicators)

Technical indicators in tti return data in various shapes depending on the indicator type:

• Single-column indicators: Most oscillators and trend indicators (e.g., AverageTrueRange, ChaikinMoneyFlow, Momentum) return a DataFrame with a single column named after the indicator (e.g., atr, cmf, mom).
• Multi-column indicators: Some indicators return multiple components. For example:
  • BollingerBands: Returns middle_band, upper_band, and lower_band.
  • DirectionalMovementIndex: Returns +di, -di, dx, adx, and adxr.
  • IchimokuCloud: Returns tenkan_sen, kijun_sen, senkou_a, and senkou_b.
  • StochasticOscillator: Returns %K and %D.
  • MovingAverageConvergenceDivergence: Returns macd and signal_line.
  • PriceChannel: Returns highest_high and lowest_low.
  • ProjectionOscillator: Returns posc and trigger_line.

The signals_simulation tool performs trading simulations for various technical indicators in the tti.indicators package. Each simulation produces a performance report containing execution time, a saved visualization graph, and detailed simulation statistics.

Simulation Statistics Fields

When reviewing simulation results, the following metrics are provided:

• number_of_trading_days: Total duration of the simulation in days.
• number_of_buy_signals: Total count of buy signals generated by the indicator.
• number_of_ignored_buy_signals: Buy signals that were not acted upon.
• number_of_sell_signals: Total count of sell signals generated by the indicator.
• number_of_ignored_sell_signals: Sell signals that were not acted upon.
• last_stock_value: The price of the stock at the end of the simulation.
• last_exposure: The total value of current market exposure.
• last_open_long_positions: Number of active long positions at the end of the simulation.
• last_open_short_positions: Number of active short positions at the end of the simulation.
• last_portfolio_value: The value of the portfolio (excluding cash/balance).
• last_earnings: Total earnings generated during the simulation.
• final_balance: The final cash balance after all trades and earnings.

To use an indicator, follow these steps:

1. Load your market data into a pandas.DataFrame with a datetime index.
2. Instantiate the specific indicator class from tti.indicators, passing your data to the input_data parameter.
3. Use the indicator's methods to retrieve data, signals, or visualizations.

Core Indicator Methods

• getTiData(): Returns the calculated indicator data as a DataFrame.
• getTiValue(date=None): Returns the indicator value for a specific date. If no date is provided, it returns the most recent value.
• getTiSignal(): Returns the current trading signal (e.g., ('buy', -1)).
• getTiGraph().show(): Generates and displays a graph of the indicator.
• getTiSimulation(...): Executes a trading simulation based on the indicator's signals.

import pandas as pd
from tti.indicators import AccumulationDistributionLine

# Load data
df = pd.read_csv('./data/SCMN.SW.csv', parse_dates=True, index_col=0)

# Initialize indicator
indicator = AccumulationDistributionLine(input_data=df)

# Access data and signals
data = indicator.getTiData()
signal = indicator.getTiSignal()
value = indicator.getTiValue()

# Visualize
indicator.getTiGraph().show()