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Merge branch 'damo1884-talib_example' into develop
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fredfortier
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# Run Command
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# catalyst run --start 2017-1-1 --end 2017-11-1 -o talib_simple.pickle -f talib_simple.py -x poloniex
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#
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# Description
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# Simple TALib Example showing how to use various indicators in you strategy
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# Based loosly on https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import talib as ta
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from logbook import Logger
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from matplotlib.dates import date2num
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from matplotlib.finance import candlestick_ohlc
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from catalyst import run_algorithm
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from catalyst.api import (
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order,
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order_target_percent,
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symbol,
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)
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from catalyst.exchange.stats_utils import get_pretty_stats
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algo_namespace = 'talib_sample'
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log = Logger(algo_namespace)
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def initialize(context):
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log.info('Starting TALib Simple Example')
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context.ASSET_NAME = 'BTC_USDT'
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context.asset = symbol(context.ASSET_NAME)
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context.ORDER_SIZE = 10
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context.SLIPPAGE_ALLOWED = 0.05
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context.swallow_errors = True
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context.errors = []
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# Bars to look at per iteration should be bigger than SMA_SLOW
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context.BARS = 365
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context.COUNT = 0
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# Technical Analysis Settings
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context.SMA_FAST = 50
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context.SMA_SLOW = 100
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context.RSI_PERIOD = 14
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context.RSI_OVER_BOUGHT = 80
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context.RSI_OVER_SOLD = 20
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context.RSI_AVG_PERIOD = 15
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context.MACD_FAST = 12
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context.MACD_SLOW = 26
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context.MACD_SIGNAL = 9
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context.STOCH_K = 14
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context.STOCH_D = 3
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context.STOCH_OVER_BOUGHT = 80
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context.STOCH_OVER_SOLD = 20
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pass
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def _handle_data(context, data):
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# Get price, open, high, low, close
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prices = data.history(
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context.asset,
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bar_count=context.BARS,
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fields=['price', 'open', 'high', 'low', 'close'],
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frequency='1d')
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# Create a analysis data frame
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analysis = pd.DataFrame(index=prices.index)
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# SMA FAST
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analysis['sma_f'] = ta.SMA(prices.close.as_matrix(), context.SMA_FAST)
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# SMA SLOW
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analysis['sma_s'] = ta.SMA(prices.close.as_matrix(), context.SMA_SLOW)
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# Relative Strength Index
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analysis['rsi'] = ta.RSI(prices.close.as_matrix(), context.RSI_PERIOD)
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# RSI SMA
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analysis['sma_r'] = ta.SMA(analysis.rsi.as_matrix(),
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context.RSI_AVG_PERIOD)
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# MACD, MACD Signal, MACD Histogram
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analysis['macd'], analysis['macdSignal'], analysis['macdHist'] = ta.MACD(
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prices.close.as_matrix(), fastperiod=context.MACD_FAST,
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slowperiod=context.MACD_SLOW, signalperiod=context.MACD_SIGNAL)
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# Stochastics %K %D
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# %K = (Current Close - Lowest Low)/(Highest High - Lowest Low) * 100
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# %D = 3-day SMA of %K
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analysis['stoch_k'], analysis['stoch_d'] = ta.STOCH(
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prices.high.as_matrix(), prices.low.as_matrix(),
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prices.close.as_matrix(), slowk_period=context.STOCH_K,
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slowd_period=context.STOCH_D)
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# SMA FAST over SLOW Crossover
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analysis['sma_test'] = np.where(analysis.sma_f > analysis.sma_s, 1, 0)
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# MACD over Signal Crossover
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analysis['macd_test'] = np.where((analysis.macd > analysis.macdSignal), 1,
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0)
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# Stochastics OVER BOUGHT & Decreasing
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analysis['stoch_over_bought'] = np.where(
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(analysis.stoch_k > context.STOCH_OVER_BOUGHT) & (
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analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
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# Stochastics OVER SOLD & Increasing
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analysis['stoch_over_sold'] = np.where(
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(analysis.stoch_k < context.STOCH_OVER_SOLD) & (
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analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
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# RSI OVER BOUGHT & Decreasing
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analysis['rsi_over_bought'] = np.where(
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(analysis.rsi > context.RSI_OVER_BOUGHT) & (
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analysis.rsi < analysis.rsi.shift(1)), 1, 0)
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# RSI OVER SOLD & Increasing
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analysis['rsi_over_sold'] = np.where(
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(analysis.rsi < context.RSI_OVER_SOLD) & (
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analysis.rsi > analysis.rsi.shift(1)), 1, 0)
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# Save the prices and analysis to send to analyze
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context.prices = prices
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context.analysis = analysis
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context.price = data.current(context.asset, 'price')
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makeOrders(context, analysis)
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# Log the values of this bar
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logAnalysis(analysis)
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def handle_data(context, data):
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log.info('handling bar {}'.format(data.current_dt))
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try:
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_handle_data(context, data)
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except Exception as e:
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log.warn('aborting the bar on error {}'.format(e))
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context.errors.append(e)
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log.info('completed bar {}, total execution errors {}'.format(
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data.current_dt,
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len(context.errors)
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))
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if len(context.errors) > 0:
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log.info('the errors:\n{}'.format(context.errors))
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def analyze(context, results):
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# Save results in CSV file
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filename = os.path.splitext(os.path.basename('talib_simple'))[0]
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results.to_csv(filename + '.csv')
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log.info('the daily stats:\n{}'.format(get_pretty_stats(results)))
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chart(context, context.prices, context.analysis, results)
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pass
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def makeOrders(context, analysis):
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if context.asset in context.portfolio.positions:
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# Current position
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position = context.portfolio.positions[context.asset]
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if (position == 0):
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log.info('Position Zero')
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return
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# Cost Basis
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cost_basis = position.cost_basis
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log.info(
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'Holdings: {amount} @ {cost_basis}'.format(
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amount=position.amount,
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cost_basis=cost_basis
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)
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)
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# Sell when holding and got sell singnal
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if isSell(context, analysis):
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profit = (context.price * position.amount) - (
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cost_basis * position.amount)
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order_target_percent(
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asset=context.asset,
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target=0,
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limit_price=context.price * (1 - context.SLIPPAGE_ALLOWED),
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)
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log.info(
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'Sold {amount} @ {price} Profit: {profit}'.format(
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amount=position.amount,
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price=context.price,
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profit=profit
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)
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)
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else:
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log.info('no buy or sell opportunity found')
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else:
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# Buy when not holding and got buy signal
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if isBuy(context, analysis):
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order(
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asset=context.asset,
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amount=context.ORDER_SIZE,
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limit_price=context.price * (1 + context.SLIPPAGE_ALLOWED)
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)
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log.info(
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'Bought {amount} @ {price}'.format(
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amount=context.ORDER_SIZE,
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price=context.price
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)
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)
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def isBuy(context, analysis):
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# Bullish SMA Crossover
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if (getLast(analysis, 'sma_test') == 1):
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# Bullish MACD
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if (getLast(analysis, 'macd_test') == 1):
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return True
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# # Bullish Stochastics
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# if(getLast(analysis, 'stoch_over_sold') == 1):
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# return True
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# # Bullish RSI
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# if(getLast(analysis, 'rsi_over_sold') == 1):
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# return True
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return False
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def isSell(context, analysis):
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# Bearish SMA Crossover
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if (getLast(analysis, 'sma_test') == 0):
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# Bearish MACD
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if (getLast(analysis, 'macd_test') == 0):
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return True
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# # Bearish Stochastics
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# if(getLast(analysis, 'stoch_over_bought') == 0):
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# return True
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# # Bearish RSI
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# if(getLast(analysis, 'rsi_over_bought') == 0):
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# return True
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return False
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def chart(context, prices, analysis, results):
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results.portfolio_value.plot()
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# Data for matplotlib finance plot
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dates = date2num(prices.index.to_pydatetime())
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# Create the Open High Low Close Tuple
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prices_ohlc = [tuple([dates[i],
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prices.open[i],
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prices.high[i],
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prices.low[i],
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prices.close[i]]) for i in range(len(dates))]
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fig = plt.figure(figsize=(14, 18))
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# Draw the candle sticks
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ax1 = fig.add_subplot(411)
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ax1.set_ylabel(context.ASSET_NAME, size=20)
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candlestick_ohlc(ax1, prices_ohlc, width=0.4, colorup='g', colordown='r')
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# Draw Moving Averages
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analysis.sma_f.plot(ax=ax1, c='r')
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analysis.sma_s.plot(ax=ax1, c='g')
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# RSI
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ax2 = fig.add_subplot(412)
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ax2.set_ylabel('RSI', size=12)
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analysis.rsi.plot(ax=ax2, c='g',
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label='Period: ' + str(context.RSI_PERIOD))
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analysis.sma_r.plot(ax=ax2, c='r',
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label='MA: ' + str(context.RSI_AVG_PERIOD))
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ax2.axhline(y=30, c='b')
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ax2.axhline(y=50, c='black')
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ax2.axhline(y=70, c='b')
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ax2.set_ylim([0, 100])
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handles, labels = ax2.get_legend_handles_labels()
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ax2.legend(handles, labels)
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# Draw MACD computed with Talib
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ax3 = fig.add_subplot(413)
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ax3.set_ylabel('MACD: ' + str(context.MACD_FAST) + ', ' + str(
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context.MACD_SLOW) + ', ' + str(context.MACD_SIGNAL), size=12)
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analysis.macd.plot(ax=ax3, color='b', label='Macd')
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analysis.macdSignal.plot(ax=ax3, color='g', label='Signal')
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analysis.macdHist.plot(ax=ax3, color='r', label='Hist')
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ax3.axhline(0, lw=2, color='0')
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handles, labels = ax3.get_legend_handles_labels()
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ax3.legend(handles, labels)
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# Stochastic plot
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ax4 = fig.add_subplot(414)
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ax4.set_ylabel('Stoch (k,d)', size=12)
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analysis.stoch_k.plot(ax=ax4, label='stoch_k:' + str(context.STOCH_K),
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color='r')
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analysis.stoch_d.plot(ax=ax4, label='stoch_d:' + str(context.STOCH_D),
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color='g')
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handles, labels = ax4.get_legend_handles_labels()
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ax4.legend(handles, labels)
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ax4.axhline(y=20, c='b')
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ax4.axhline(y=50, c='black')
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ax4.axhline(y=80, c='b')
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plt.show()
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def logAnalysis(analysis):
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# Log only the last value in the array
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log.info('- sma_f: {:.2f}'.format(getLast(analysis, 'sma_f')))
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log.info('- sma_s: {:.2f}'.format(getLast(analysis, 'sma_s')))
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log.info('- rsi: {:.2f}'.format(getLast(analysis, 'rsi')))
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log.info('- sma_r: {:.2f}'.format(getLast(analysis, 'sma_r')))
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log.info('- macd: {:.2f}'.format(getLast(analysis, 'macd')))
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log.info(
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'- macdSignal: {:.2f}'.format(getLast(analysis, 'macdSignal')))
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log.info('- macdHist: {:.2f}'.format(getLast(analysis, 'macdHist')))
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log.info('- stoch_k: {:.2f}'.format(getLast(analysis, 'stoch_k')))
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log.info('- stoch_d: {:.2f}'.format(getLast(analysis, 'stoch_d')))
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log.info('- sma_test: {}'.format(getLast(analysis, 'sma_test')))
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log.info('- macd_test: {}'.format(getLast(analysis, 'macd_test')))
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log.info('- stoch_over_bought: {}'.format(
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getLast(analysis, 'stoch_over_bought')))
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log.info(
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'- stoch_over_sold: {}'.format(getLast(analysis, 'stoch_over_sold')))
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log.info('- rsi_over_bought: {}'.format(
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getLast(analysis, 'rsi_over_bought')))
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log.info(
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'- rsi_over_sold: {}'.format(getLast(analysis, 'rsi_over_sold')))
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def getLast(arr, name):
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return arr[name][arr[name].index[-1]]
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if __name__ == '__main__':
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run_algorithm(
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capital_base=10000,
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data_frequency='daily',
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initialize=initialize,
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handle_data=handle_data,
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analyze=analyze,
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exchange_name='poloniex',
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base_currency='usdt',
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start=pd.to_datetime('2016-11-1', utc=True),
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end=pd.to_datetime('2017-11-10', utc=True),
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)
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