def metadata_function(config: dict): print(f"Getting Metadata for funds...") print(f"") data, fund_list = function_data_download(config) for fund in fund_list: if fund != '^GSPC': get_api_metadata(fund, data=data[fund], plot_output=True)
def risk_function(config: dict): print(f"Risk Factors for funds...\r\n") config['tickers'] += ' ^GSPC ^IRX' data, fund_list = function_data_download(config) for fund in fund_list: if fund != '^GSPC' and fund != '^IRX': print( f"Risk Factors of {TICKER}{fund}{NORMAL}...") meta_fund = get_api_metadata(fund, function='info') match_fund, match_data = function_sector_match( meta_fund, data[fund], config) if match_fund is not None: match_data = match_data[match_fund] risk_comparison(data[fund], data['^GSPC'], data['^IRX'], print_out=True, sector_data=match_data)
def run_dev(script: list): """Run Development Script Script that is for implementing new content Arguments: script {list} -- dataset, funds, periods, config Returns: dict -- analysis object of fund data """ dataset = script[0] funds = script[1] periods = script[2] config = script[3] # Start of automated process analysis = {} clock = start_clock() for fund_name in funds: if fund_name in SKIP_INDEXES: continue fund_print = INDEXES.get(fund_name, fund_name) print("") print(f"~~{fund_print}~~") create_sub_temp_dir(fund_name, sub_periods=config['period']) analysis[fund_name] = {} analysis[fund_name]['metadata'] = get_api_metadata( fund_name, max_close=max(dataset[periods[0]][fund_name]['Close']), data=dataset[periods[0]][fund_name]) ###################### START OF PERIOD LOOPING ############################# for i, period in enumerate(periods): fund_data = {} fund = dataset[period][fund_name] start = date_extractor(fund.index[0], _format='str') end = date_extractor(fund.index[-1], _format='str') fund_data['dates_covered'] = {'start': str(start), 'end': str(end)} fund_data['name'] = fund_name fund_print2 = fund_print + f" ({period}) " p = ProgressBar(config['process_steps'], name=fund_print2, offset=clock) p.start() fund_data['statistics'] = get_high_level_stats(fund) fund_data['clustered_osc'] = cluster_oscs(fund, function='all', filter_thresh=3, name=fund_name, plot_output=False, progress_bar=p, view=period) fund_data['full_stochastic'] = full_stochastic(fund, name=fund_name, plot_output=False, out_suppress=False, progress_bar=p, view=period) fund_data['rsi'] = RSI(fund, name=fund_name, plot_output=False, out_suppress=False, progress_bar=p, view=period) fund_data['ultimate'] = ultimate_oscillator(fund, name=fund_name, plot_output=False, out_suppress=False, progress_bar=p, view=period) fund_data['awesome'] = awesome_oscillator(fund, name=fund_name, plot_output=False, progress_bar=p, view=period) fund_data['momentum_oscillator'] = momentum_oscillator( fund, name=fund_name, plot_output=False, progress_bar=p, view=period) fund_data['on_balance_volume'] = on_balance_volume( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['simple_moving_average'] = triple_moving_average( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['exp_moving_average'] = triple_exp_mov_average( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['sma_swing_trade'] = moving_average_swing_trade( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['ema_swing_trade'] = moving_average_swing_trade( fund, function='ema', plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['hull_moving_average'] = hull_moving_average( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['macd'] = mov_avg_convergence_divergence( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['bear_bull_power'] = bear_bull_power(fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['total_power'] = total_power(fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['bollinger_bands'] = bollinger_bands(fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['commodity_channels'] = commodity_channel_index( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['rate_of_change'] = rate_of_change_oscillator( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['know_sure_thing'] = know_sure_thing(fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['average_true_range'] = average_true_range( fund, plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['adx'] = average_directional_index( fund, atr=fund_data['average_true_range']['tabular'], plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['parabolic_sar'] = parabolic_sar( fund, adx_tabular=fund_data['adx']['tabular'], plot_output=False, name=fund_name, progress_bar=p, view=period) fund_data['demand_index'] = demand_index(fund, plot_output=False, name=fund_name, progress_bar=p, view=period) if 'no_index' not in config['state']: strength, match_data = relative_strength( fund_name, full_data_dict=dataset[period], config=config, plot_output=False, meta=analysis[fund_name]['metadata'], progress_bar=p, period=period, interval=config['interval'][i], view=period) fund_data['relative_strength'] = strength fund_data['statistics']['risk_ratios'] = risk_comparison( fund, dataset[period]['^GSPC'], dataset[period]['^IRX'], sector_data=match_data) p.uptick() # Support and Resistance Analysis fund_data['support_resistance'] = find_resistance_support_lines( fund, name=fund_name, plot_output=False, progress_bar=p, view=period) # Feature Detection Block fund_data['features'] = {} fund_data['features'][ 'head_shoulders'] = feature_detection_head_and_shoulders( fund, name=fund_name, plot_output=False, progress_bar=p, view=period) fund_data['candlesticks'] = candlesticks(fund, name=fund_name, plot_output=False, view=period, progress_bar=p) fund_data['price_gaps'] = analyze_price_gaps(fund, name=fund_name, plot_output=False, progress_bar=p, view=period) # Get Trendlines fund_data['trendlines'] = get_trendlines( fund, name=fund_name, plot_output=False, progress_bar=p, view=period, meta=analysis[fund_name]['metadata']) # Various Fund-specific Metrics fund_data['futures'] = future_returns(fund, progress_bar=p) # Parse through indicators and pull out latest signals (must be last) fund_data['last_signals'] = assemble_last_signals( fund_data, fund=fund, name=fund_name, view=period, progress_bar=p, plot_output=False) p.end() analysis[fund_name][period] = fund_data analysis[fund_name]['synopsis'] = generate_synopsis(analysis, name=fund_name) return analysis, clock